Project

Creating Regularized Logistic Regression Models.

Considering Our Initial train_data and test_data. Train data has 80% and test data has 20%

We can see that our Target variable Churn is highly Imbalanced, we’ll use SMOTE or Class Weights

Our target variable Churn is highly imbalanced lets use class Weights. Later, we’ll also use smote for tree based models.

tab = table(train_data$Churn)
tab


  No  Yes 
4140 1496

w_yes = sum(tab)/(tab["Yes"]*2)
w_no = sum(tab)/(tab["No"]*2)

cw = ifelse(train_data$Churn=="Yes", w_yes, w_no)

Lasso Logistic Regression

set.seed(1)
ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary)
lasso <- train(Churn ~., data = train_data, method = "glmnet", 
               trControl=ctrl,
               tuneGrid = expand.grid(alpha = 1, lambda = 10^seq(-3, 3, length = 100)), weights = cw, verbose = FALSE, metric = "ROC")

set.seed(1)
lasso_preds=predict(lasso, test_data)
confusionMatrix(lasso_preds,test_data$Churn, positive = "Yes", mode = "everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  750  89
       Yes 284 284
                                         
               Accuracy : 0.7349         
                 95% CI : (0.711, 0.7578)
    No Information Rate : 0.7349         
    P-Value [Acc > NIR] : 0.5139         
                                         
                  Kappa : 0.417          
                                         
 Mcnemar's Test P-Value : <2e-16         
                                         
            Sensitivity : 0.7614         
            Specificity : 0.7253         
         Pos Pred Value : 0.5000         
         Neg Pred Value : 0.8939         
              Precision : 0.5000         
                 Recall : 0.7614         
                     F1 : 0.6036         
             Prevalence : 0.2651         
         Detection Rate : 0.2018         
   Detection Prevalence : 0.4037         
      Balanced Accuracy : 0.7434         
                                         
       'Positive' Class : Yes

set.seed(1)
lasso_predictions_prob=predict(lasso, test_data,type = "prob")
head(lasso_predictions_prob)

set.seed(1)
pred_t_lasso <- prediction(lasso_predictions_prob$Yes,test_data$Churn)
performance(pred_t_lasso, measure = "auc")@y.values

[[1]]
[1] 0.8258578

set.seed(1)
coef(lasso$finalModel, lasso$bestTune$lambda)

35 x 1 sparse Matrix of class "dgCMatrix"
                                                s1
(Intercept)                           3.891233e-01
genderMale                           -2.372213e-02
SeniorCitizenYes                      2.610790e-01
PartnerYes                           -6.936093e-02
DependentsYes                        -1.109678e-01
tenure1-2 years                      -7.077641e-01
tenure2-3years                       -9.553924e-01
tenure3-4 years                      -7.308061e-01
tenure4-5 years                      -9.151902e-01
tenure5-6years                       -9.171379e-01
PhoneServiceYes                      -1.252608e-01
MultipleLinesNo phone service         1.188959e-13
MultipleLinesYes                      2.377907e-01
InternetServiceFiber optic            9.521603e-01
InternetServiceNo                    -7.279652e-01
OnlineSecurityNo internet service    -1.398205e-14
OnlineSecurityYes                    -3.695177e-01
OnlineBackupNo internet service      -1.915067e-01
OnlineBackupYes                      -5.080314e-02
DeviceProtectionNo internet service  -1.868537e-02
DeviceProtectionYes                   2.478629e-02
TechSupportNo internet service       -2.265448e-04
TechSupportYes                       -2.514879e-01
StreamingTVNo internet service       -6.450532e-05
StreamingTVYes                        3.563487e-01
StreamingMoviesNo internet service   -2.756541e-06
StreamingMoviesYes                    3.001080e-01
ContractOne year                     -7.798632e-01
ContractTwo year                     -1.624907e+00
PaperlessBillingYes                   3.242571e-01
PaymentMethodCredit card (automatic)  .           
PaymentMethodElectronic check         3.510701e-01
PaymentMethodMailed check             7.317061e-02
MonthlyCharges                        .           
TotalCharges                         -1.666476e-04

Lasso shrinks 2 variables to 0. which indicates the variable is not significant.

The AUC of the model is 82.5 The metrics for Churn = Yes : Precision - 0.50 Recall - 0.76 F1 Score - 0.60

set.seed(1)
ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary)
ridge <- train(
Churn ~., data = train_data, method = "glmnet",
trControl = ctrl,
tuneGrid = expand.grid(alpha = 0, lambda = 10^seq(-3, 3, length =
100)), weights = cw, verbose = FALSE, metric = "ROC")

set.seed(1)
ridge_preds=predict(ridge, test_data)
confusionMatrix(ridge_preds,test_data$Churn,positive = "Yes", mode= "everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  747  88
       Yes 287 285
                                          
               Accuracy : 0.7335          
                 95% CI : (0.7095, 0.7564)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 0.5618          
                                          
                  Kappa : 0.4156          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.7641          
            Specificity : 0.7224          
         Pos Pred Value : 0.4983          
         Neg Pred Value : 0.8946          
              Precision : 0.4983          
                 Recall : 0.7641          
                     F1 : 0.6032          
             Prevalence : 0.2651          
         Detection Rate : 0.2026          
   Detection Prevalence : 0.4065          
      Balanced Accuracy : 0.7433          
                                          
       'Positive' Class : Yes

set.seed(1)
ridge_preds_prob=predict(ridge, test_data,type = "prob")
pred_t_ridge <- prediction(ridge_preds_prob$Yes,test_data$Churn)
performance(pred_t_ridge, measure = "auc")@y.values

[[1]]
[1] 0.824857

set.seed(1)
coef(ridge$finalModel, ridge$bestTune$lambda)

35 x 1 sparse Matrix of class "dgCMatrix"
                                                s1
(Intercept)                           0.1675575599
genderMale                           -0.0432328420
SeniorCitizenYes                      0.2629471016
PartnerYes                           -0.1123862884
DependentsYes                        -0.1367018767
tenure1-2 years                      -0.5506075122
tenure2-3years                       -0.7646105161
tenure3-4 years                      -0.5782583149
tenure4-5 years                      -0.7540519345
tenure5-6years                       -0.7824578108
PhoneServiceYes                      -0.0861226640
MultipleLinesNo phone service         0.0813460534
MultipleLinesYes                      0.1744984557
InternetServiceFiber optic            0.6866559967
InternetServiceNo                    -0.1342331906
OnlineSecurityNo internet service    -0.1351961897
OnlineSecurityYes                    -0.4315799951
OnlineBackupNo internet service      -0.1365245116
OnlineBackupYes                      -0.1303689142
DeviceProtectionNo internet service  -0.1375164282
DeviceProtectionYes                  -0.0224649735
TechSupportNo internet service       -0.1380805434
TechSupportYes                       -0.3329431682
StreamingTVNo internet service       -0.1389593737
StreamingTVYes                        0.2515931139
StreamingMoviesNo internet service   -0.1388537907
StreamingMoviesYes                    0.2033830121
ContractOne year                     -0.6959238743
ContractTwo year                     -1.3017078826
PaperlessBillingYes                   0.3159197373
PaymentMethodCredit card (automatic) -0.0526270399
PaymentMethodElectronic check         0.3577456070
PaymentMethodMailed check             0.0895394080
MonthlyCharges                        0.0056118351
TotalCharges                         -0.0001549136

The AUC of the model is 82.4 The metrics for Churn = Yes : Precision - 0.49 Recall - 0.76 F1 Score - 0.60

Enet

set.seed(1)
ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary)
enet <- train(
Churn ~., data = train_data, method = "glmnet",
trControl = ctrl,
tuneGrid = expand.grid(alpha =seq(0,1, length=10), lambda =
10^seq(-3, 3, length = 100)),weights = cw, verbose = FALSE, metric = "ROC")

set.seed(1)
enet_predictions=predict(enet, test_data)
confusionMatrix(enet_predictions,test_data$Churn,positive = "Yes", mode ="everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  751  89
       Yes 283 284
                                          
               Accuracy : 0.7356          
                 95% CI : (0.7117, 0.7585)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 0.4898          
                                          
                  Kappa : 0.4182          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.7614          
            Specificity : 0.7263          
         Pos Pred Value : 0.5009          
         Neg Pred Value : 0.8940          
              Precision : 0.5009          
                 Recall : 0.7614          
                     F1 : 0.6043          
             Prevalence : 0.2651          
         Detection Rate : 0.2018          
   Detection Prevalence : 0.4030          
      Balanced Accuracy : 0.7438          
                                          
       'Positive' Class : Yes

set.seed(1)
enet_predictions_prob=predict(enet, test_data,type = "prob")
pred_t_enet <- prediction(enet_predictions_prob$Yes,test_data$Churn)
performance(pred_t_enet, measure = "auc")@y.values

[[1]]
[1] 0.8253289

set.seed(1)
coef(enet$finalModel, enet$bestTune$lambda)

35 x 1 sparse Matrix of class "dgCMatrix"
                                                s1
(Intercept)                           0.1703601891
genderMale                           -0.0327545629
SeniorCitizenYes                      0.2633564444
PartnerYes                           -0.0876658176
DependentsYes                        -0.1216508152
tenure1-2 years                      -0.6450044864
tenure2-3years                       -0.8769624049
tenure3-4 years                      -0.6644534242
tenure4-5 years                      -0.8422499942
tenure5-6years                       -0.8529417695
PhoneServiceYes                      -0.1044091139
MultipleLinesNo phone service         0.0903296767
MultipleLinesYes                      0.2012480797
InternetServiceFiber optic            0.7800488374
InternetServiceNo                    -0.1194813511
OnlineSecurityNo internet service    -0.1212466017
OnlineSecurityYes                    -0.4104469888
OnlineBackupNo internet service      -0.1241099651
OnlineBackupYes                      -0.0974481231
DeviceProtectionNo internet service  -0.1265745523
DeviceProtectionYes                   .           
TechSupportNo internet service       -0.1287647131
TechSupportYes                       -0.3008534424
StreamingTVNo internet service       -0.1311511103
StreamingTVYes                        0.2893013148
StreamingMoviesNo internet service   -0.1302622093
StreamingMoviesYes                    0.2357194897
ContractOne year                     -0.7477220424
ContractTwo year                     -1.4912558423
PaperlessBillingYes                   0.3225755880
PaymentMethodCredit card (automatic) -0.0147787625
PaymentMethodElectronic check         0.3591554187
PaymentMethodMailed check             0.0867236066
MonthlyCharges                        0.0049361240
TotalCharges                         -0.0001652435

Enet shrinks one variable to zero.

The AUC of the model is 82.5 The metrics for Churn = Yes : Precision - 0.50 Recall - 0.76 F1 Score - 0.60

Random Forest with Class weights

set.seed(1)
rf <- randomForest(Churn ~ ., data = train_data)
rf


Call:
 randomForest(formula = Churn ~ ., data = train_data) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 4

        OOB estimate of  error rate: 20.19%
Confusion matrix:
      No Yes class.error
No  3724 416   0.1004831
Yes  722 774   0.4826203

The out of bag error here is 20.19% which is low.

set.seed(1)
ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary)
grid_rf <- expand.grid(mtry = c(2, 4, 8, 16))

set.seed(1)
m_rf <- train(Churn ~ ., data = train_data, method = "rf", trControl = ctrl, tuneGrid = grid_rf,importance=T,weights = cw, verbose = FALSE, metric = "ROC")
m_rf

Random Forest 

5636 samples
  19 predictor
   2 classes: 'No', 'Yes' 

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 5073, 5073, 5072, 5073, 5073, 5072, ... 
Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   2    0.8342401  0.9458937  0.3782819
   4    0.8377427  0.8992754  0.5367248
   8    0.8354715  0.8990338  0.5233647
  16    0.8288062  0.8946860  0.5146532

ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 4.

The Auc for random forest with class weights is 83.7 at mtry = 4.

set.seed(1)
rf_predictions=predict(m_rf, test_data)
table(rf_predictions, test_data$Churn)

              
rf_predictions  No Yes
           No  916 192
           Yes 118 181

set.seed(1)
rf_predictions_prob=predict(m_rf, test_data, type="prob")
pred_t_rf=prediction(rf_predictions_prob$Yes,test_data$Churn)
performance(pred_t_rf, measure = "auc")@y.values

[[1]]
[1] 0.812759

set.seed(1)
confusionMatrix(rf_predictions,test_data$Churn,positive = "Yes", mode = "everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  916 192
       Yes 118 181
                                          
               Accuracy : 0.7797          
                 95% CI : (0.7571, 0.8011)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 6.050e-05       
                                          
                  Kappa : 0.3963          
                                          
 Mcnemar's Test P-Value : 3.382e-05       
                                          
            Sensitivity : 0.4853          
            Specificity : 0.8859          
         Pos Pred Value : 0.6054          
         Neg Pred Value : 0.8267          
              Precision : 0.6054          
                 Recall : 0.4853          
                     F1 : 0.5387          
             Prevalence : 0.2651          
         Detection Rate : 0.1286          
   Detection Prevalence : 0.2125          
      Balanced Accuracy : 0.6856          
                                          
       'Positive' Class : Yes

The AUC of the model is 81.2 The metrics for Churn = Yes : Precision - 0.60 Recall - 0.48 F1 Score - 0.53

varImp(m_rf)

rf variable importance

  only 20 most important variables shown (out of 34)

Let’s also Consider smote class imbalance method

set.seed(1)
ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary,sampling = "smote")
grid_rf <- expand.grid(mtry = c(2, 4, 8, 16))

set.seed(1)
smote_rf <- train(Churn ~ ., data = train_data, method = "rf", trControl = ctrl, tuneGrid = grid_rf,importance=T, verbose = FALSE, metric = "ROC")

Loading required package: recipes

Attaching package: ‘recipes’

The following object is masked from ‘package:Matrix’:

    update

The following object is masked from ‘package:stats’:

    step

smote_rf

Random Forest 

5636 samples
  19 predictor
   2 classes: 'No', 'Yes' 

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 5073, 5073, 5072, 5073, 5073, 5072, ... 
Addtional sampling using SMOTE

Resampling results across tuning parameters:

  mtry  ROC        Sens       Spec     
   2    0.8391561  0.8659420  0.6048277
   4    0.8417882  0.8840580  0.5700940
   8    0.8332172  0.8852657  0.5367025
  16    0.8296352  0.8869565  0.5280134

ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 4.

The best AUC is 84.1 at mtry = 4

set.seed(1)
smote_rf_predictions=predict(smote_rf, test_data)
table(smote_rf_predictions, test_data$Churn)

                    
smote_rf_predictions  No Yes
                 No  892 179
                 Yes 142 194

set.seed(1)
smote_rf_predictions_prob=predict(smote_rf, test_data, type="prob")
pred_smote_rf=prediction(smote_rf_predictions_prob$Yes,test_data$Churn)
performance(pred_smote_rf, measure = "auc")@y.values

[[1]]
[1] 0.8240688

set.seed(1)
confusionMatrix(smote_rf_predictions,test_data$Churn,positive = "Yes", mode="everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  892 179
       Yes 142 194
                                         
               Accuracy : 0.7719         
                 95% CI : (0.749, 0.7936)
    No Information Rate : 0.7349         
    P-Value [Acc > NIR] : 0.0008023      
                                         
                  Kappa : 0.3953         
                                         
 Mcnemar's Test P-Value : 0.0445028      
                                         
            Sensitivity : 0.5201         
            Specificity : 0.8627         
         Pos Pred Value : 0.5774         
         Neg Pred Value : 0.8329         
              Precision : 0.5774         
                 Recall : 0.5201         
                     F1 : 0.5472         
             Prevalence : 0.2651         
         Detection Rate : 0.1379         
   Detection Prevalence : 0.2388         
      Balanced Accuracy : 0.6914         
                                         
       'Positive' Class : Yes

varImp(m_rf)

rf variable importance

  only 20 most important variables shown (out of 34)

The AUC of the model is 82.4 The metrics for Churn = Yes : Precision - 0.57 Recall - 0.52 F1 Score - 0.54

varImp(smote_rf)

rf variable importance

  only 20 most important variables shown (out of 34)

Gradient boosting machine class weights

ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary)
set.seed(1)
gbm <- train(
Churn ~., data = train_data, method = "gbm",
trControl = ctrl,weights = cw,verbose=FALSE, metric ="ROC")

gbm

Stochastic Gradient Boosting 

5636 samples
  19 predictor
   2 classes: 'No', 'Yes' 

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 5073, 5073, 5072, 5073, 5073, 5072, ... 
Resampling results across tuning parameters:

  interaction.depth  n.trees  ROC        Sens       Spec     
  1                   50      0.8444198  0.7318841  0.7993960
  1                  100      0.8501394  0.7359903  0.8067472
  1                  150      0.8518877  0.7350242  0.8067517
  2                   50      0.8507579  0.7437198  0.7967159
  2                  100      0.8513875  0.7461353  0.7973915
  2                  150      0.8504270  0.7509662  0.7880492
  3                   50      0.8518242  0.7519324  0.7960850
  3                  100      0.8515457  0.7538647  0.7880582
  3                  150      0.8498536  0.7521739  0.7847204

Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode'
 was held constant at a value of 10
ROC was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 150, interaction.depth = 1, shrinkage = 0.1
 and n.minobsinnode = 10.

set.seed(1)
gbm_predictions=predict(gbm, test_data)
table(gbm_predictions, test_data$Churn)

               
gbm_predictions  No Yes
            No  739  75
            Yes 295 298

set.seed(1)
gbm_predictions_prob=predict(gbm, test_data, type="prob")
pred_t_gbm=prediction(gbm_predictions_prob$Yes,test_data$Churn)
performance(pred_t_gbm, measure = "auc")@y.values

[[1]]
[1] 0.8338022

confusionMatrix(gbm_predictions,test_data$Churn,positive = "Yes", mode= "everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  739  75
       Yes 295 298
                                          
               Accuracy : 0.737           
                 95% CI : (0.7132, 0.7599)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 0.4418          
                                          
                  Kappa : 0.4322          
                                          
 Mcnemar's Test P-Value : <2e-16          
                                          
            Sensitivity : 0.7989          
            Specificity : 0.7147          
         Pos Pred Value : 0.5025          
         Neg Pred Value : 0.9079          
              Precision : 0.5025          
                 Recall : 0.7989          
                     F1 : 0.6170          
             Prevalence : 0.2651          
         Detection Rate : 0.2118          
   Detection Prevalence : 0.4215          
      Balanced Accuracy : 0.7568          
                                          
       'Positive' Class : Yes

The AUC of the model is 83.3 The metrics for Churn = Yes : Precision - 0.50 Recall - 0.80 F1 Score - 0.61

Gradient Boosting machine Using smote.


ctrl=trainControl(method="cv", number=10, classProbs = TRUE, summaryFunction = twoClassSummary,
sampling="smote")
set.seed(1)
smote_gbm<-train(Churn ~ ., data = train_data, method = "gbm", verbose=FALSE, metric ="ROC",
trControl= ctrl)

set.seed(1)
smote_gbm_predictions=predict(smote_gbm, test_data)
table(smote_gbm_predictions, test_data$Churn)

                     
smote_gbm_predictions  No Yes
                  No  889 152
                  Yes 145 221

set.seed(1)
s_gbm_predictions_prob=predict(smote_gbm, test_data, type="prob")
pred_s_gbm=prediction(s_gbm_predictions_prob$Yes,test_data$Churn)
performance(pred_s_gbm, measure = "auc")@y.values

[[1]]
[1] 0.8319782

confusionMatrix(smote_gbm_predictions,test_data$Churn,positive = "Yes", mode ="everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  889 152
       Yes 145 221
                                        
               Accuracy : 0.7889        
                 95% CI : (0.7666, 0.81)
    No Information Rate : 0.7349        
    P-Value [Acc > NIR] : 1.539e-06     
                                        
                  Kappa : 0.455         
                                        
 Mcnemar's Test P-Value : 0.7277        
                                        
            Sensitivity : 0.5925        
            Specificity : 0.8598        
         Pos Pred Value : 0.6038        
         Neg Pred Value : 0.8540        
              Precision : 0.6038        
                 Recall : 0.5925        
                     F1 : 0.5981        
             Prevalence : 0.2651        
         Detection Rate : 0.1571        
   Detection Prevalence : 0.2601        
      Balanced Accuracy : 0.7261        
                                        
       'Positive' Class : Yes

The AUC of the model is 83.1 The metrics for Churn = Yes : Precision - 0.60 Recall - 0.59 F1 Score - 0.59

SVM Models - Class weights

ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary,preProc=c("center","scale"))
set.seed(1)
svml <- train(Churn ~., data = train_data, method = "svmLinear", 
              trControl = ctrl,weights = cw, verbose=FALSE, metric ="ROC" )

svml

Support Vector Machines with Linear Kernel 

5636 samples
  19 predictor
   2 classes: 'No', 'Yes' 

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 5073, 5073, 5072, 5073, 5073, 5072, ... 
Resampling results:

  ROC        Sens       Spec     
  0.8297696  0.9166667  0.4658434

Tuning parameter 'C' was held constant at a value of 1

set.seed(1)
svml_predictions=predict(svml, test_data)
table(svml_predictions, test_data$Churn)

                
svml_predictions  No Yes
             No  925 221
             Yes 109 152

set.seed(1)
svml_predictions_prob = predict(svml, test_data, type = "prob")
svml_y_predictions = prediction(svml_predictions_prob$Yes, test_data$Churn)
performance(svml_y_predictions, measure = "auc")@y.values

[[1]]
[1] 0.8073037

confusionMatrix(svml_predictions, test_data$Churn, positive = 'Yes', mode="everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  925 221
       Yes 109 152
                                          
               Accuracy : 0.7655          
                 95% CI : (0.7424, 0.7874)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 0.004726        
                                          
                  Kappa : 0.3342          
                                          
 Mcnemar's Test P-Value : 9.941e-10       
                                          
            Sensitivity : 0.4075          
            Specificity : 0.8946          
         Pos Pred Value : 0.5824          
         Neg Pred Value : 0.8072          
              Precision : 0.5824          
                 Recall : 0.4075          
                     F1 : 0.4795          
             Prevalence : 0.2651          
         Detection Rate : 0.1080          
   Detection Prevalence : 0.1855          
      Balanced Accuracy : 0.6510          
                                          
       'Positive' Class : Yes

The AUC of the model is 80.0 The metrics for Churn = Yes : Precision - 0.58 Recall - 0.40 F1 Score - 0.47

SVMRadial

ctrl= trainControl(method ="cv", number = 10,classProbs = TRUE, summaryFunction = twoClassSummary,preProc=c("center","scale"))
set.seed(1)
svmr <- train(Churn ~., data = train_data, method = "svmRadial", 
              trControl = ctrl,weights = cw,verbose=FALSE, metric ="ROC" )

set.seed(1)
svmr_predictions_prob = predict(svmr, test_data, type = "prob")
svmr_y_predictions = prediction(svmr_predictions_prob$Yes, test_data$Churn)
performance(svmr_y_predictions, measure = "auc")@y.values

[[1]]
[1] 0.7983429

set.seed(1)
svmr_predictions=predict(svmr, test_data)
table(svmr_predictions, test_data$Churn)

                
svmr_predictions  No Yes
             No  944 228
             Yes  90 145

set.seed(1)
confusionMatrix(svmr_predictions, test_data$Churn, positive = 'Yes', mode = "everything")

Confusion Matrix and Statistics

          Reference
Prediction  No Yes
       No  944 228
       Yes  90 145
                                          
               Accuracy : 0.774           
                 95% CI : (0.7512, 0.7956)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 0.0004152       
                                          
                  Kappa : 0.3422          
                                          
 Mcnemar's Test P-Value : 1.559e-14       
                                          
            Sensitivity : 0.3887          
            Specificity : 0.9130          
         Pos Pred Value : 0.6170          
         Neg Pred Value : 0.8055          
              Precision : 0.6170          
                 Recall : 0.3887          
                     F1 : 0.4770          
             Prevalence : 0.2651          
         Detection Rate : 0.1031          
   Detection Prevalence : 0.1670          
      Balanced Accuracy : 0.6508          
                                          
       'Positive' Class : Yes

The AUC of the model is 79 The metrics for Churn = Yes : Precision - 0.61 Recall - 0.38 F1 Score - 0.47

compare=resamples(list(L=lasso,R=ridge,E=enet,Rf= m_rf, smoterf= smote_rf, Gbm=gbm,smotegbm = smote_gbm, svmLinear=svml, svmRadial = svmr))
summary(compare)


Call:
summary.resamples(object = compare)

Models: L, R, E, Rf, smoterf, Gbm, smotegbm, svmLinear, svmRadial 
Number of resamples: 10 

ROC 
               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
L         0.8143825 0.8433883 0.8505717 0.8466964 0.8590982 0.8693559    0
R         0.8145446 0.8410620 0.8506361 0.8467245 0.8579670 0.8714976    0
E         0.8146905 0.8427642 0.8508132 0.8468400 0.8586353 0.8700805    0
Rf        0.8081899 0.8259852 0.8393209 0.8377427 0.8496035 0.8710548    0
smoterf   0.8158658 0.8335400 0.8388203 0.8417882 0.8519887 0.8675845    0
Gbm       0.8242308 0.8469061 0.8536594 0.8518877 0.8643136 0.8744605    0
smotegbm  0.8252440 0.8454390 0.8521699 0.8508602 0.8587037 0.8706844    0
svmLinear 0.7968583 0.8244196 0.8334574 0.8297696 0.8360366 0.8684058    0
svmRadial 0.7900010 0.8217364 0.8255096 0.8262664 0.8367532 0.8711111    0

Sens 
               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
L         0.7028986 0.7210145 0.7439614 0.7376812 0.7518116 0.7657005    0
R         0.7077295 0.7264493 0.7403382 0.7371981 0.7506039 0.7632850    0
E         0.7053140 0.7228261 0.7439614 0.7384058 0.7518116 0.7705314    0
Rf        0.8695652 0.8876812 0.9070048 0.8992754 0.9118357 0.9202899    0
smoterf   0.8478261 0.8804348 0.8864734 0.8840580 0.8925121 0.9130435    0
Gbm       0.7101449 0.7179952 0.7367150 0.7350242 0.7518116 0.7632850    0
smotegbm  0.8405797 0.8544686 0.8671498 0.8664251 0.8798309 0.8937198    0
svmLinear 0.8913043 0.9082126 0.9190821 0.9166667 0.9263285 0.9396135    0
svmRadial 0.9009662 0.9142512 0.9251208 0.9268116 0.9384058 0.9565217    0

Spec 
               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
L         0.7181208 0.7852349 0.7966667 0.7967293 0.8163423 0.8400000    0
R         0.7248322 0.7896644 0.8100000 0.8027472 0.8196980 0.8400000    0
E         0.7181208 0.7900000 0.8026846 0.8000761 0.8163423 0.8400000    0
Rf        0.4496644 0.5309843 0.5485011 0.5367248 0.5592617 0.5733333    0
smoterf   0.4832215 0.5492617 0.5718792 0.5700940 0.6050000 0.6133333    0
Gbm       0.7382550 0.7872595 0.8066667 0.8067517 0.8358613 0.8666667    0
smotegbm  0.5436242 0.6113870 0.6400000 0.6276063 0.6527517 0.6666667    0
svmLinear 0.4026846 0.4500000 0.4697987 0.4658434 0.4916667 0.5066667    0
svmRadial 0.3422819 0.4187472 0.4530201 0.4444430 0.4700000 0.5133333    0

Creating a Neural Network Model

td<-train_data
ts<-test_data

str(td)

'data.frame':   5636 obs. of  20 variables:
 $ gender          : Factor w/ 2 levels "Female","Male": 1 2 1 1 2 1 1 2 2 2 ...
 $ SeniorCitizen   : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
 $ Partner         : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 2 1 2 2 ...
 $ Dependents      : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 2 2 1 ...
 $ tenure          : Factor w/ 6 levels "0-1 years","1-2 years",..: 1 4 1 1 2 1 3 6 2 5 ...
 $ PhoneService    : Factor w/ 2 levels "No","Yes": 1 1 2 2 2 1 2 2 2 2 ...
 $ MultipleLines   : Factor w/ 3 levels "No","No phone service",..: 2 2 1 3 3 2 3 1 1 3 ...
 $ InternetService : Factor w/ 3 levels "DSL","Fiber optic",..: 1 1 2 2 2 1 2 1 1 2 ...
 $ OnlineSecurity  : Factor w/ 3 levels "No","No internet service",..: 1 3 1 1 1 3 1 3 3 1 ...
 $ OnlineBackup    : Factor w/ 3 levels "No","No internet service",..: 3 1 1 1 3 1 1 3 1 1 ...
 $ DeviceProtection: Factor w/ 3 levels "No","No internet service",..: 1 3 1 3 1 1 3 1 1 3 ...
 $ TechSupport     : Factor w/ 3 levels "No","No internet service",..: 1 3 1 1 1 1 3 1 1 1 ...
 $ StreamingTV     : Factor w/ 3 levels "No","No internet service",..: 1 1 1 3 3 1 3 1 1 3 ...
 $ StreamingMovies : Factor w/ 3 levels "No","No internet service",..: 1 1 1 3 1 1 3 1 1 3 ...
 $ Contract        : Factor w/ 3 levels "Month-to-month",..: 1 2 1 1 1 1 1 2 1 2 ...
 $ PaperlessBilling: Factor w/ 2 levels "No","Yes": 2 1 2 2 2 1 2 1 2 1 ...
 $ PaymentMethod   : Factor w/ 4 levels "Bank transfer (automatic)",..: 3 1 3 3 2 4 3 1 4 2 ...
 $ MonthlyCharges  : num  29.9 42.3 70.7 99.7 89.1 ...
 $ TotalCharges    : num  29.9 1840.8 151.7 820.5 1949.4 ...
 $ Churn           : Factor w/ 2 levels "No","Yes": 1 1 2 2 1 1 2 1 1 1 ...

factor_levels <- lapply(td[sapply(td, is.factor)], levels)


print(factor_levels)

$gender
[1] "Female" "Male"  

$SeniorCitizen
[1] "No"  "Yes"

$Partner
[1] "No"  "Yes"

$Dependents
[1] "No"  "Yes"

$tenure
[1] "0-1 years" "1-2 years" "2-3years"  "3-4 years" "4-5 years" "5-6years" 

$PhoneService
[1] "No"  "Yes"

$MultipleLines
[1] "No"               "No phone service" "Yes"             

$InternetService
[1] "DSL"         "Fiber optic" "No"         

$OnlineSecurity
[1] "No"                  "No internet service" "Yes"                

$OnlineBackup
[1] "No"                  "No internet service" "Yes"                

$DeviceProtection
[1] "No"                  "No internet service" "Yes"                

$TechSupport
[1] "No"                  "No internet service" "Yes"                

$StreamingTV
[1] "No"                  "No internet service" "Yes"                

$StreamingMovies
[1] "No"                  "No internet service" "Yes"                

$Contract
[1] "Month-to-month" "One year"       "Two year"      

$PaperlessBilling
[1] "No"  "Yes"

$PaymentMethod
[1] "Bank transfer (automatic)" "Credit card (automatic)"   "Electronic check"         
[4] "Mailed check"             

$Churn
[1] "No"  "Yes"

Lets convert the target variable to 0 & 1

td$Churn=as.numeric(td$Churn)-1

Converting Ordered columns to numeric


td$tenure <- as.numeric(factor(td$tenure, levels =
c("0-1 years", "1-2 years", "2-3years",  "3-4 years", "4-5 years", "5-6years" ), ordered = TRUE))-1

labels<-td$Churn


td=data.frame(one_hot(data.table(td)))

str(td)

'data.frame':   5636 obs. of  47 variables:
 $ gender_Female                          : int  1 0 1 1 0 1 1 0 0 0 ...
 $ gender_Male                            : int  0 1 0 0 1 0 0 1 1 1 ...
 $ SeniorCitizen_No                       : int  1 1 1 1 1 1 1 1 1 1 ...
 $ SeniorCitizen_Yes                      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ Partner_No                             : int  0 1 1 1 1 1 0 1 0 0 ...
 $ Partner_Yes                            : int  1 0 0 0 0 0 1 0 1 1 ...
 $ Dependents_No                          : int  1 1 1 1 0 1 1 0 0 1 ...
 $ Dependents_Yes                         : int  0 0 0 0 1 0 0 1 1 0 ...
 $ tenure                                 : num  0 3 0 0 1 0 2 5 1 4 ...
 $ PhoneService_No                        : int  1 1 0 0 0 1 0 0 0 0 ...
 $ PhoneService_Yes                       : int  0 0 1 1 1 0 1 1 1 1 ...
 $ MultipleLines_No                       : int  0 0 1 0 0 0 0 1 1 0 ...
 $ MultipleLines_No.phone.service         : int  1 1 0 0 0 1 0 0 0 0 ...
 $ MultipleLines_Yes                      : int  0 0 0 1 1 0 1 0 0 1 ...
 $ InternetService_DSL                    : int  1 1 0 0 0 1 0 1 1 0 ...
 $ InternetService_Fiber.optic            : int  0 0 1 1 1 0 1 0 0 1 ...
 $ InternetService_No                     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ OnlineSecurity_No                      : int  1 0 1 1 1 0 1 0 0 1 ...
 $ OnlineSecurity_No.internet.service     : int  0 0 0 0 0 0 0 0 0 0 ...
 $ OnlineSecurity_Yes                     : int  0 1 0 0 0 1 0 1 1 0 ...
 $ OnlineBackup_No                        : int  0 1 1 1 0 1 1 0 1 1 ...
 $ OnlineBackup_No.internet.service       : int  0 0 0 0 0 0 0 0 0 0 ...
 $ OnlineBackup_Yes                       : int  1 0 0 0 1 0 0 1 0 0 ...
 $ DeviceProtection_No                    : int  1 0 1 0 1 1 0 1 1 0 ...
 $ DeviceProtection_No.internet.service   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ DeviceProtection_Yes                   : int  0 1 0 1 0 0 1 0 0 1 ...
 $ TechSupport_No                         : int  1 0 1 1 1 1 0 1 1 1 ...
 $ TechSupport_No.internet.service        : int  0 0 0 0 0 0 0 0 0 0 ...
 $ TechSupport_Yes                        : int  0 1 0 0 0 0 1 0 0 0 ...
 $ StreamingTV_No                         : int  1 1 1 0 0 1 0 1 1 0 ...
 $ StreamingTV_No.internet.service        : int  0 0 0 0 0 0 0 0 0 0 ...
 $ StreamingTV_Yes                        : int  0 0 0 1 1 0 1 0 0 1 ...
 $ StreamingMovies_No                     : int  1 1 1 0 1 1 0 1 1 0 ...
 $ StreamingMovies_No.internet.service    : int  0 0 0 0 0 0 0 0 0 0 ...
 $ StreamingMovies_Yes                    : int  0 0 0 1 0 0 1 0 0 1 ...
 $ Contract_Month.to.month                : int  1 0 1 1 1 1 1 0 1 0 ...
 $ Contract_One.year                      : int  0 1 0 0 0 0 0 1 0 1 ...
 $ Contract_Two.year                      : int  0 0 0 0 0 0 0 0 0 0 ...
 $ PaperlessBilling_No                    : int  0 1 0 0 0 1 0 1 0 1 ...
 $ PaperlessBilling_Yes                   : int  1 0 1 1 1 0 1 0 1 0 ...
 $ PaymentMethod_Bank.transfer..automatic.: int  0 1 0 0 0 0 0 1 0 0 ...
 $ PaymentMethod_Credit.card..automatic.  : int  0 0 0 0 1 0 0 0 0 1 ...
 $ PaymentMethod_Electronic.check         : int  1 0 1 1 0 0 1 0 0 0 ...
 $ PaymentMethod_Mailed.check             : int  0 0 0 0 0 1 0 0 1 0 ...
 $ MonthlyCharges                         : num  29.9 42.3 70.7 99.7 89.1 ...
 $ TotalCharges                           : num  29.9 1840.8 151.7 820.5 1949.4 ...
 $ Churn                                  : num  0 0 1 1 0 0 1 0 0 0 ...

td$Churn<-NULL

set.seed(1)
in_train=createDataPartition(labels, p=0.9, list=FALSE)
x_train=td[in_train,]
y_train= labels[in_train]
x_test=td[-in_train,]
y_test= labels[-in_train]

numeric_cols<-names(x_train[sapply(x_train, is.numeric) & !sapply(x_train, is.integer)])
col_means_train <- attr(scale(x_train[,numeric_cols]), "scaled:center")
col_stddevs_train <- attr( scale(x_train[,numeric_cols]), "scaled:scale")
x_train[numeric_cols]= scale(x_train[numeric_cols])
x_test[numeric_cols]<- scale(x_test[numeric_cols], center = col_means_train,
scale = col_stddevs_train)

library(keras)
set.seed(1)
model <- keras_model_sequential() %>%
layer_dense(units = 16, activation = "relu",
input_shape = dim(x_train)[2]) %>%
layer_dropout(0.2)%>%
layer_dense(units = 32,activation="relu" ) %>%
layer_dropout(0.5)%>%
layer_dense(units = 1, activation="sigmoid")

2024-05-06 14:21:05.369455: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

set.seed(1)
model %>% compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = "acc")
model

Model: "sequential"
_________________________________________________________________________________________________________
 Layer (type)                                  Output Shape                              Param #         
=========================================================================================================
 dense_2 (Dense)                               (None, 16)                                752             
 dropout_1 (Dropout)                           (None, 16)                                0               
 dense_1 (Dense)                               (None, 32)                                544             
 dropout (Dropout)                             (None, 32)                                0               
 dense (Dense)                                 (None, 1)                                 33              
=========================================================================================================
Total params: 1329 (5.19 KB)
Trainable params: 1329 (5.19 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________________________________________________

set.seed(1)
history <- model %>% fit(
  as.matrix(x_train), y_train, epochs = 30, batch_size = 50,verbose = 2, validation_data=list(as.matrix(x_test), y_test))

Epoch 1/30
102/102 - 6s - loss: 0.5574 - acc: 0.7108 - val_loss: 0.4822 - val_acc: 0.7176 - 6s/epoch - 57ms/step
Epoch 2/30
102/102 - 0s - loss: 0.4757 - acc: 0.7577 - val_loss: 0.4528 - val_acc: 0.7655 - 455ms/epoch - 4ms/step
Epoch 3/30
102/102 - 0s - loss: 0.4507 - acc: 0.7865 - val_loss: 0.4376 - val_acc: 0.7940 - 446ms/epoch - 4ms/step
Epoch 4/30
102/102 - 0s - loss: 0.4495 - acc: 0.7922 - val_loss: 0.4345 - val_acc: 0.7993 - 412ms/epoch - 4ms/step
Epoch 5/30
102/102 - 0s - loss: 0.4435 - acc: 0.7909 - val_loss: 0.4343 - val_acc: 0.7940 - 395ms/epoch - 4ms/step
Epoch 6/30
102/102 - 0s - loss: 0.4385 - acc: 0.7974 - val_loss: 0.4352 - val_acc: 0.7886 - 397ms/epoch - 4ms/step
Epoch 7/30
102/102 - 0s - loss: 0.4371 - acc: 0.7958 - val_loss: 0.4351 - val_acc: 0.7940 - 398ms/epoch - 4ms/step
Epoch 8/30
102/102 - 0s - loss: 0.4345 - acc: 0.8015 - val_loss: 0.4336 - val_acc: 0.7904 - 382ms/epoch - 4ms/step
Epoch 9/30
102/102 - 1s - loss: 0.4284 - acc: 0.8021 - val_loss: 0.4379 - val_acc: 0.7957 - 525ms/epoch - 5ms/step
Epoch 10/30
102/102 - 1s - loss: 0.4283 - acc: 0.8039 - val_loss: 0.4333 - val_acc: 0.7975 - 510ms/epoch - 5ms/step
Epoch 11/30
102/102 - 0s - loss: 0.4312 - acc: 0.8072 - val_loss: 0.4367 - val_acc: 0.7869 - 475ms/epoch - 5ms/step
Epoch 12/30
102/102 - 0s - loss: 0.4206 - acc: 0.8047 - val_loss: 0.4497 - val_acc: 0.7744 - 382ms/epoch - 4ms/step
Epoch 13/30
102/102 - 0s - loss: 0.4272 - acc: 0.8035 - val_loss: 0.4356 - val_acc: 0.7993 - 367ms/epoch - 4ms/step
Epoch 14/30
102/102 - 0s - loss: 0.4214 - acc: 0.8078 - val_loss: 0.4386 - val_acc: 0.7922 - 473ms/epoch - 5ms/step
Epoch 15/30
102/102 - 0s - loss: 0.4201 - acc: 0.8068 - val_loss: 0.4353 - val_acc: 0.7975 - 367ms/epoch - 4ms/step
Epoch 16/30
102/102 - 0s - loss: 0.4220 - acc: 0.8047 - val_loss: 0.4356 - val_acc: 0.7993 - 494ms/epoch - 5ms/step
Epoch 17/30
102/102 - 0s - loss: 0.4221 - acc: 0.8062 - val_loss: 0.4395 - val_acc: 0.7904 - 382ms/epoch - 4ms/step
Epoch 18/30
102/102 - 0s - loss: 0.4245 - acc: 0.8033 - val_loss: 0.4414 - val_acc: 0.7869 - 446ms/epoch - 4ms/step
Epoch 19/30
102/102 - 0s - loss: 0.4204 - acc: 0.8104 - val_loss: 0.4402 - val_acc: 0.7886 - 382ms/epoch - 4ms/step
Epoch 20/30
102/102 - 0s - loss: 0.4205 - acc: 0.8068 - val_loss: 0.4412 - val_acc: 0.7904 - 384ms/epoch - 4ms/step
Epoch 21/30
102/102 - 0s - loss: 0.4180 - acc: 0.8054 - val_loss: 0.4429 - val_acc: 0.7780 - 443ms/epoch - 4ms/step
Epoch 22/30
102/102 - 0s - loss: 0.4217 - acc: 0.8074 - val_loss: 0.4454 - val_acc: 0.7851 - 397ms/epoch - 4ms/step
Epoch 23/30
102/102 - 0s - loss: 0.4186 - acc: 0.8116 - val_loss: 0.4417 - val_acc: 0.7886 - 478ms/epoch - 5ms/step
Epoch 24/30
102/102 - 0s - loss: 0.4182 - acc: 0.8084 - val_loss: 0.4394 - val_acc: 0.7886 - 384ms/epoch - 4ms/step
Epoch 25/30
102/102 - 0s - loss: 0.4206 - acc: 0.8094 - val_loss: 0.4406 - val_acc: 0.7886 - 382ms/epoch - 4ms/step
Epoch 26/30
102/102 - 0s - loss: 0.4173 - acc: 0.8131 - val_loss: 0.4390 - val_acc: 0.7975 - 442ms/epoch - 4ms/step
Epoch 27/30
102/102 - 0s - loss: 0.4118 - acc: 0.8094 - val_loss: 0.4405 - val_acc: 0.7851 - 496ms/epoch - 5ms/step
Epoch 28/30
102/102 - 0s - loss: 0.4125 - acc: 0.8121 - val_loss: 0.4392 - val_acc: 0.7851 - 442ms/epoch - 4ms/step
Epoch 29/30
102/102 - 0s - loss: 0.4135 - acc: 0.8100 - val_loss: 0.4414 - val_acc: 0.7851 - 382ms/epoch - 4ms/step
Epoch 30/30
102/102 - 0s - loss: 0.4119 - acc: 0.8149 - val_loss: 0.4418 - val_acc: 0.7904 - 445ms/epoch - 4ms/step

set.seed(1)
predicted_probs = model%>% predict(as.matrix(x_test))


 1/18 [>.............................] - ETA: 1s
18/18 [==============================] - 0s 1ms/step

18/18 [==============================] - 0s 1ms/step

preds_labels = factor(ifelse(predicted_probs>0.5, "1", "0"))
confusionMatrix(preds_labels, as.factor(y_test),  positive="1",mode = "everything")

Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 375  89
         1  29  70
                                          
               Accuracy : 0.7904          
                 95% CI : (0.7544, 0.8233)
    No Information Rate : 0.7176          
    P-Value [Acc > NIR] : 4.847e-05       
                                          
                  Kappa : 0.4161          
                                          
 Mcnemar's Test P-Value : 5.592e-08       
                                          
            Sensitivity : 0.4403          
            Specificity : 0.9282          
         Pos Pred Value : 0.7071          
         Neg Pred Value : 0.8082          
              Precision : 0.7071          
                 Recall : 0.4403          
                     F1 : 0.5426          
             Prevalence : 0.2824          
         Detection Rate : 0.1243          
   Detection Prevalence : 0.1758          
      Balanced Accuracy : 0.6842          
                                          
       'Positive' Class : 1

The Auc of the model is 79 the model metrics for positive class precision = 0.70 recall = 0.44 f1score = 0.54

Let’s use class weights

library(keras)
set.seed(1)
model2 <- keras_model_sequential() %>%
layer_dense(units = 16, activation = "relu",
input_shape = dim(x_train)[2]) %>%
layer_dropout(0.2)%>%
layer_dense(units = 32,activation="relu" ) %>%
layer_dropout(0.5)%>%
layer_dense(units = 1, activation="sigmoid")

set.seed(1)
model2 %>% compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = "acc")
model2

Model: "sequential_1"
_________________________________________________________________________________________________________
 Layer (type)                                  Output Shape                              Param #         
=========================================================================================================
 dense_5 (Dense)                               (None, 16)                                752             
 dropout_3 (Dropout)                           (None, 16)                                0               
 dense_4 (Dense)                               (None, 32)                                544             
 dropout_2 (Dropout)                           (None, 32)                                0               
 dense_3 (Dense)                               (None, 1)                                 33              
=========================================================================================================
Total params: 1329 (5.19 KB)
Trainable params: 1329 (5.19 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________________________________________________

set.seed(1)
history <- model2 %>% fit(
  as.matrix(x_train), y_train, epochs = 30, batch_size = 50,verbose = 2, validation_data=list(as.matrix(x_test), y_test),class_weight = list("0"=w_no, "1"=w_yes))

Epoch 1/30
102/102 - 2s - loss: 0.6014 - acc: 0.6700 - val_loss: 0.5104 - val_acc: 0.7496 - 2s/epoch - 20ms/step
Epoch 2/30
102/102 - 0s - loss: 0.5457 - acc: 0.7116 - val_loss: 0.5101 - val_acc: 0.7442 - 472ms/epoch - 5ms/step
Epoch 3/30
102/102 - 1s - loss: 0.5317 - acc: 0.7225 - val_loss: 0.4947 - val_acc: 0.7655 - 557ms/epoch - 5ms/step
Epoch 4/30
102/102 - 1s - loss: 0.5267 - acc: 0.7323 - val_loss: 0.4890 - val_acc: 0.7655 - 539ms/epoch - 5ms/step
Epoch 5/30
102/102 - 0s - loss: 0.5082 - acc: 0.7520 - val_loss: 0.5016 - val_acc: 0.7496 - 462ms/epoch - 5ms/step
Epoch 6/30
102/102 - 0s - loss: 0.5044 - acc: 0.7449 - val_loss: 0.4921 - val_acc: 0.7549 - 415ms/epoch - 4ms/step
Epoch 7/30
102/102 - 1s - loss: 0.5049 - acc: 0.7560 - val_loss: 0.4895 - val_acc: 0.7567 - 585ms/epoch - 6ms/step
Epoch 8/30
102/102 - 0s - loss: 0.4976 - acc: 0.7481 - val_loss: 0.4876 - val_acc: 0.7567 - 399ms/epoch - 4ms/step
Epoch 9/30
102/102 - 1s - loss: 0.5029 - acc: 0.7475 - val_loss: 0.4824 - val_acc: 0.7620 - 507ms/epoch - 5ms/step
Epoch 10/30
102/102 - 0s - loss: 0.5004 - acc: 0.7568 - val_loss: 0.4710 - val_acc: 0.7709 - 383ms/epoch - 4ms/step
Epoch 11/30
102/102 - 0s - loss: 0.4906 - acc: 0.7556 - val_loss: 0.4722 - val_acc: 0.7691 - 493ms/epoch - 5ms/step
Epoch 12/30
102/102 - 0s - loss: 0.4962 - acc: 0.7550 - val_loss: 0.4872 - val_acc: 0.7655 - 396ms/epoch - 4ms/step
Epoch 13/30
102/102 - 0s - loss: 0.4894 - acc: 0.7560 - val_loss: 0.4680 - val_acc: 0.7762 - 462ms/epoch - 5ms/step
Epoch 14/30
102/102 - 0s - loss: 0.4919 - acc: 0.7637 - val_loss: 0.4874 - val_acc: 0.7620 - 397ms/epoch - 4ms/step
Epoch 15/30
102/102 - 0s - loss: 0.4920 - acc: 0.7564 - val_loss: 0.4822 - val_acc: 0.7620 - 443ms/epoch - 4ms/step
Epoch 16/30
102/102 - 0s - loss: 0.4934 - acc: 0.7550 - val_loss: 0.4827 - val_acc: 0.7655 - 412ms/epoch - 4ms/step
Epoch 17/30
102/102 - 0s - loss: 0.4864 - acc: 0.7534 - val_loss: 0.4838 - val_acc: 0.7620 - 382ms/epoch - 4ms/step
Epoch 18/30
102/102 - 0s - loss: 0.4870 - acc: 0.7599 - val_loss: 0.4792 - val_acc: 0.7673 - 460ms/epoch - 5ms/step
Epoch 19/30
102/102 - 0s - loss: 0.4894 - acc: 0.7607 - val_loss: 0.4759 - val_acc: 0.7762 - 383ms/epoch - 4ms/step
Epoch 20/30
102/102 - 0s - loss: 0.4882 - acc: 0.7644 - val_loss: 0.4805 - val_acc: 0.7620 - 476ms/epoch - 5ms/step
Epoch 21/30
102/102 - 0s - loss: 0.4828 - acc: 0.7642 - val_loss: 0.4792 - val_acc: 0.7691 - 380ms/epoch - 4ms/step
Epoch 22/30
102/102 - 0s - loss: 0.4866 - acc: 0.7587 - val_loss: 0.4821 - val_acc: 0.7638 - 382ms/epoch - 4ms/step
Epoch 23/30
102/102 - 0s - loss: 0.4828 - acc: 0.7613 - val_loss: 0.4757 - val_acc: 0.7691 - 459ms/epoch - 4ms/step
Epoch 24/30
102/102 - 0s - loss: 0.4801 - acc: 0.7597 - val_loss: 0.4767 - val_acc: 0.7638 - 397ms/epoch - 4ms/step
Epoch 25/30
102/102 - 0s - loss: 0.4855 - acc: 0.7564 - val_loss: 0.4789 - val_acc: 0.7673 - 444ms/epoch - 4ms/step
Epoch 26/30
102/102 - 0s - loss: 0.4876 - acc: 0.7629 - val_loss: 0.4782 - val_acc: 0.7620 - 380ms/epoch - 4ms/step
Epoch 27/30
102/102 - 0s - loss: 0.4832 - acc: 0.7611 - val_loss: 0.4779 - val_acc: 0.7602 - 415ms/epoch - 4ms/step
Epoch 28/30
102/102 - 0s - loss: 0.4878 - acc: 0.7617 - val_loss: 0.4782 - val_acc: 0.7602 - 397ms/epoch - 4ms/step
Epoch 29/30
102/102 - 0s - loss: 0.4792 - acc: 0.7597 - val_loss: 0.4716 - val_acc: 0.7655 - 383ms/epoch - 4ms/step
Epoch 30/30
102/102 - 0s - loss: 0.4806 - acc: 0.7589 - val_loss: 0.4844 - val_acc: 0.7567 - 459ms/epoch - 4ms/step

set.seed(1)
predicted_probs = model2%>% predict(as.matrix(x_test))


 1/18 [>.............................] - ETA: 1s
18/18 [==============================] - 0s 1ms/step

18/18 [==============================] - 0s 1ms/step

preds_labels = factor(ifelse(predicted_probs>0.5, "1", "0"))
confusionMatrix(preds_labels, as.factor(y_test),  positive="1",mode = "everything")

Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 299  32
         1 105 127
                                         
               Accuracy : 0.7567         
                 95% CI : (0.719, 0.7916)
    No Information Rate : 0.7176         
    P-Value [Acc > NIR] : 0.0209         
                                         
                  Kappa : 0.473          
                                         
 Mcnemar's Test P-Value : 7.681e-10      
                                         
            Sensitivity : 0.7987         
            Specificity : 0.7401         
         Pos Pred Value : 0.5474         
         Neg Pred Value : 0.9033         
              Precision : 0.5474         
                 Recall : 0.7987         
                     F1 : 0.6496         
             Prevalence : 0.2824         
         Detection Rate : 0.2256         
   Detection Prevalence : 0.4121         
      Balanced Accuracy : 0.7694         
                                         
       'Positive' Class : 1

The auc of the model is 75

The metrics of model for yes class : precision = 0.54 recall = 0.79 f1score = 0.64

Comparatively model with class weights Recall value is higher than imbalanced class lets consider training hyper parameter tuning using class weights

Tune-Hyperparameters

library(tfruns)
set.seed(1)
runs <- tuning_run("Projectscript.R",
                   flags = list(
                     nodes = c(16, 32, 64),
                     nodes2 = c(16, 32, 64),
                     learning_rate = c(0.01, 0.001),
                     batch_size = c(20, 50),
                     epochs = c(30, 50),
                     activation = c("relu", "sigmoid", "tanh"),
                     activation2 = c("relu", "sigmoid", "tanh"),
                     dropout = c(0.2, 0.5),
                     dropout2 = c(0.2, 0.5)
                   ), sample = 0.02
                   )

2,592 total combinations of flags 
(sampled to 52 combinations)

Training run 1/52 (flags = list(64, 64, 0.01, 20, 30, "tanh", "sigmoid", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-33-47Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:44 - loss: 1.0439 - acc: 0.6500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5496 - acc: 0.7171

254/254 [==============================] - 3s 6ms/step - loss: 0.5496 - acc: 0.7171 - val_loss: 0.5016 - val_acc: 0.7549
Epoch 2/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5021 - acc: 0.7530

254/254 [==============================] - 1s 4ms/step - loss: 0.5021 - acc: 0.7530 - val_loss: 0.4930 - val_acc: 0.7638
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4997 - acc: 0.7506

254/254 [==============================] - 1s 4ms/step - loss: 0.4997 - acc: 0.7506 - val_loss: 0.5012 - val_acc: 0.7531
Epoch 4/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4986 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4986 - acc: 0.7556 - val_loss: 0.4770 - val_acc: 0.7691
Epoch 5/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4944 - acc: 0.7487

254/254 [==============================] - 1s 4ms/step - loss: 0.4944 - acc: 0.7487 - val_loss: 0.4936 - val_acc: 0.7549
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4921 - acc: 0.7512

254/254 [==============================] - 1s 4ms/step - loss: 0.4921 - acc: 0.7512 - val_loss: 0.5334 - val_acc: 0.7265
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4950 - acc: 0.7475

254/254 [==============================] - 1s 4ms/step - loss: 0.4950 - acc: 0.7475 - val_loss: 0.4899 - val_acc: 0.7496
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4949 - acc: 0.7518

254/254 [==============================] - 1s 4ms/step - loss: 0.4949 - acc: 0.7518 - val_loss: 0.4911 - val_acc: 0.7531
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4874 - acc: 0.7499

254/254 [==============================] - 1s 4ms/step - loss: 0.4874 - acc: 0.7499 - val_loss: 0.4926 - val_acc: 0.7549
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4918 - acc: 0.7534

254/254 [==============================] - 1s 4ms/step - loss: 0.4918 - acc: 0.7534 - val_loss: 0.4883 - val_acc: 0.7584
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4919 - acc: 0.7437

254/254 [==============================] - 1s 4ms/step - loss: 0.4919 - acc: 0.7437 - val_loss: 0.4748 - val_acc: 0.7655
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4867 - acc: 0.7569

254/254 [==============================] - 1s 4ms/step - loss: 0.4867 - acc: 0.7569 - val_loss: 0.4790 - val_acc: 0.7602
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4850 - acc: 0.7546

254/254 [==============================] - 1s 5ms/step - loss: 0.4850 - acc: 0.7546 - val_loss: 0.4912 - val_acc: 0.7531
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4911 - acc: 0.7467

254/254 [==============================] - 1s 4ms/step - loss: 0.4911 - acc: 0.7467 - val_loss: 0.4816 - val_acc: 0.7638
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4835 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4835 - acc: 0.7575 - val_loss: 0.4974 - val_acc: 0.7549
Epoch 16/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4880 - acc: 0.7528

254/254 [==============================] - 1s 4ms/step - loss: 0.4880 - acc: 0.7528 - val_loss: 0.4848 - val_acc: 0.7638
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4836 - acc: 0.7530

254/254 [==============================] - 1s 4ms/step - loss: 0.4836 - acc: 0.7530 - val_loss: 0.5187 - val_acc: 0.7407
Epoch 18/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4839 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.4839 - acc: 0.7514 - val_loss: 0.4885 - val_acc: 0.7567
Epoch 19/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4829 - acc: 0.7552

254/254 [==============================] - 1s 4ms/step - loss: 0.4829 - acc: 0.7552 - val_loss: 0.4709 - val_acc: 0.7726
Epoch 20/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4792 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4792 - acc: 0.7575 - val_loss: 0.4982 - val_acc: 0.7620
Epoch 21/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4826 - acc: 0.7560

254/254 [==============================] - 1s 4ms/step - loss: 0.4826 - acc: 0.7560 - val_loss: 0.4846 - val_acc: 0.7602
Epoch 22/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4824 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4824 - acc: 0.7556 - val_loss: 0.4703 - val_acc: 0.7762
Epoch 23/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4818 - acc: 0.7569

254/254 [==============================] - 1s 4ms/step - loss: 0.4818 - acc: 0.7569 - val_loss: 0.4970 - val_acc: 0.7549
Epoch 24/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4818 - acc: 0.7573

254/254 [==============================] - 1s 4ms/step - loss: 0.4818 - acc: 0.7573 - val_loss: 0.4962 - val_acc: 0.7602
Epoch 25/30

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243/254 [===========================>..] - ETA: 0s - loss: 0.4824 - acc: 0.7549
254/254 [==============================] - 1s 3ms/step - loss: 0.4838 - acc: 0.7558

254/254 [==============================] - 1s 4ms/step - loss: 0.4838 - acc: 0.7558 - val_loss: 0.5081 - val_acc: 0.7496
Epoch 26/30

  1/254 [..............................] - ETA: 0s - loss: 0.3986 - acc: 0.7500
 18/254 [=>............................] - ETA: 0s - loss: 0.4316 - acc: 0.7694
 36/254 [===>..........................] - ETA: 0s - loss: 0.4571 - acc: 0.7500
 53/254 [=====>........................] - ETA: 0s - loss: 0.4857 - acc: 0.7557
 70/254 [=======>......................] - ETA: 0s - loss: 0.4863 - acc: 0.7550
 88/254 [=========>....................] - ETA: 0s - loss: 0.4887 - acc: 0.7511
107/254 [===========>..................] - ETA: 0s - loss: 0.4920 - acc: 0.7486
126/254 [=============>................] - ETA: 0s - loss: 0.4966 - acc: 0.7448
140/254 [===============>..............] - ETA: 0s - loss: 0.4944 - acc: 0.7436
158/254 [=================>............] - ETA: 0s - loss: 0.4913 - acc: 0.7440
176/254 [===================>..........] - ETA: 0s - loss: 0.4851 - acc: 0.7486
191/254 [=====================>........] - ETA: 0s - loss: 0.4872 - acc: 0.7490
211/254 [=======================>......] - ETA: 0s - loss: 0.4848 - acc: 0.7512
230/254 [==========================>...] - ETA: 0s - loss: 0.4846 - acc: 0.7526
248/254 [============================>.] - ETA: 0s - loss: 0.4829 - acc: 0.7573
254/254 [==============================] - 1s 3ms/step - loss: 0.4815 - acc: 0.7583

254/254 [==============================] - 1s 4ms/step - loss: 0.4815 - acc: 0.7583 - val_loss: 0.5141 - val_acc: 0.7496
Epoch 27/30

  1/254 [..............................] - ETA: 0s - loss: 0.3186 - acc: 0.8000
 18/254 [=>............................] - ETA: 0s - loss: 0.4590 - acc: 0.7611
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 53/254 [=====>........................] - ETA: 0s - loss: 0.4708 - acc: 0.7604
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 89/254 [=========>....................] - ETA: 0s - loss: 0.4741 - acc: 0.7562
107/254 [===========>..................] - ETA: 0s - loss: 0.4769 - acc: 0.7589
126/254 [=============>................] - ETA: 0s - loss: 0.4769 - acc: 0.7583
143/254 [===============>..............] - ETA: 0s - loss: 0.4793 - acc: 0.7584
161/254 [==================>...........] - ETA: 0s - loss: 0.4819 - acc: 0.7559
166/254 [==================>...........] - ETA: 0s - loss: 0.4815 - acc: 0.7545
185/254 [====================>.........] - ETA: 0s - loss: 0.4759 - acc: 0.7592
199/254 [======================>.......] - ETA: 0s - loss: 0.4756 - acc: 0.7598
216/254 [========================>.....] - ETA: 0s - loss: 0.4781 - acc: 0.7560
233/254 [==========================>...] - ETA: 0s - loss: 0.4792 - acc: 0.7552
250/254 [============================>.] - ETA: 0s - loss: 0.4817 - acc: 0.7552
254/254 [==============================] - 1s 3ms/step - loss: 0.4813 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4813 - acc: 0.7556 - val_loss: 0.4901 - val_acc: 0.7620
Epoch 28/30

  1/254 [..............................] - ETA: 1s - loss: 0.4752 - acc: 0.7000
 18/254 [=>............................] - ETA: 0s - loss: 0.5065 - acc: 0.7361
 37/254 [===>..........................] - ETA: 0s - loss: 0.4912 - acc: 0.7622
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 90/254 [=========>....................] - ETA: 0s - loss: 0.4515 - acc: 0.7700
107/254 [===========>..................] - ETA: 0s - loss: 0.4688 - acc: 0.7650
126/254 [=============>................] - ETA: 0s - loss: 0.4657 - acc: 0.7687
144/254 [================>.............] - ETA: 0s - loss: 0.4669 - acc: 0.7663
163/254 [==================>...........] - ETA: 0s - loss: 0.4741 - acc: 0.7610
180/254 [====================>.........] - ETA: 0s - loss: 0.4785 - acc: 0.7592
198/254 [======================>.......] - ETA: 0s - loss: 0.4810 - acc: 0.7523
216/254 [========================>.....] - ETA: 0s - loss: 0.4836 - acc: 0.7519
234/254 [==========================>...] - ETA: 0s - loss: 0.4813 - acc: 0.7543
252/254 [============================>.] - ETA: 0s - loss: 0.4810 - acc: 0.7536
254/254 [==============================] - 1s 3ms/step - loss: 0.4802 - acc: 0.7540

254/254 [==============================] - 1s 4ms/step - loss: 0.4802 - acc: 0.7540 - val_loss: 0.4756 - val_acc: 0.7691
Epoch 29/30

  1/254 [..............................] - ETA: 0s - loss: 0.5777 - acc: 0.7000
 19/254 [=>............................] - ETA: 0s - loss: 0.4588 - acc: 0.7868
 38/254 [===>..........................] - ETA: 0s - loss: 0.4827 - acc: 0.7632
 55/254 [=====>........................] - ETA: 0s - loss: 0.4801 - acc: 0.7582
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109/254 [===========>..................] - ETA: 0s - loss: 0.4822 - acc: 0.7638
128/254 [==============>...............] - ETA: 0s - loss: 0.4841 - acc: 0.7617
145/254 [================>.............] - ETA: 0s - loss: 0.4804 - acc: 0.7628
163/254 [==================>...........] - ETA: 0s - loss: 0.4731 - acc: 0.7641
181/254 [====================>.........] - ETA: 0s - loss: 0.4817 - acc: 0.7622
199/254 [======================>.......] - ETA: 0s - loss: 0.4771 - acc: 0.7638
218/254 [========================>.....] - ETA: 0s - loss: 0.4743 - acc: 0.7651
236/254 [==========================>...] - ETA: 0s - loss: 0.4768 - acc: 0.7623
254/254 [==============================] - 1s 3ms/step - loss: 0.4768 - acc: 0.7631

254/254 [==============================] - 1s 4ms/step - loss: 0.4768 - acc: 0.7631 - val_loss: 0.4856 - val_acc: 0.7638
Epoch 30/30

  1/254 [..............................] - ETA: 1s - loss: 0.6415 - acc: 0.8000
 16/254 [>.............................] - ETA: 0s - loss: 0.5317 - acc: 0.7312
 35/254 [===>..........................] - ETA: 0s - loss: 0.5085 - acc: 0.7371
 53/254 [=====>........................] - ETA: 0s - loss: 0.4959 - acc: 0.7509
 72/254 [=======>......................] - ETA: 0s - loss: 0.4958 - acc: 0.7493
 91/254 [=========>....................] - ETA: 0s - loss: 0.5017 - acc: 0.7467
105/254 [===========>..................] - ETA: 0s - loss: 0.4985 - acc: 0.7529
123/254 [=============>................] - ETA: 0s - loss: 0.4915 - acc: 0.7565
142/254 [===============>..............] - ETA: 0s - loss: 0.4845 - acc: 0.7574
156/254 [=================>............] - ETA: 0s - loss: 0.4844 - acc: 0.7567
174/254 [===================>..........] - ETA: 0s - loss: 0.4849 - acc: 0.7580
192/254 [=====================>........] - ETA: 0s - loss: 0.4763 - acc: 0.7602
211/254 [=======================>......] - ETA: 0s - loss: 0.4763 - acc: 0.7597
228/254 [=========================>....] - ETA: 0s - loss: 0.4745 - acc: 0.7623
245/254 [===========================>..] - ETA: 0s - loss: 0.4827 - acc: 0.7612
254/254 [==============================] - 1s 3ms/step - loss: 0.4797 - acc: 0.7619

254/254 [==============================] - 1s 4ms/step - loss: 0.4797 - acc: 0.7619 - val_loss: 0.5044 - val_acc: 0.7442


Run completed: runs/2024-05-06T19-33-47Z

Training run 2/52 (flags = list(16, 32, 0.001, 50, 30, "relu", "relu", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-34-22Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 1:59 - loss: 0.6180 - acc: 0.6400

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0285s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0285s). Check your callbacks.


 20/102 [====>.........................] - ETA: 0s - loss: 0.7073 - acc: 0.6190  
 38/102 [==========>...................] - ETA: 0s - loss: 0.6789 - acc: 0.6147
 54/102 [==============>...............] - ETA: 0s - loss: 0.6728 - acc: 0.6119
 72/102 [====================>.........] - ETA: 0s - loss: 0.6645 - acc: 0.6111
 90/102 [=========================>....] - ETA: 0s - loss: 0.6560 - acc: 0.6196
102/102 [==============================] - 1s 3ms/step - loss: 0.6427 - acc: 0.6274

102/102 [==============================] - 2s 11ms/step - loss: 0.6427 - acc: 0.6274 - val_loss: 0.5621 - val_acc: 0.7282
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.5455 - acc: 0.7400
 11/102 [==>...........................] - ETA: 0s - loss: 0.5809 - acc: 0.6691
 27/102 [======>.......................] - ETA: 0s - loss: 0.5752 - acc: 0.7000
 43/102 [===========>..................] - ETA: 0s - loss: 0.5646 - acc: 0.7102
 62/102 [=================>............] - ETA: 0s - loss: 0.5734 - acc: 0.7048
 80/102 [======================>.......] - ETA: 0s - loss: 0.5740 - acc: 0.7048
 92/102 [==========================>...] - ETA: 0s - loss: 0.5711 - acc: 0.7083
102/102 [==============================] - 0s 3ms/step - loss: 0.5679 - acc: 0.7071

102/102 [==============================] - 1s 6ms/step - loss: 0.5679 - acc: 0.7071 - val_loss: 0.5415 - val_acc: 0.7336
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.5928 - acc: 0.6400
 19/102 [====>.........................] - ETA: 0s - loss: 0.5756 - acc: 0.6737
 38/102 [==========>...................] - ETA: 0s - loss: 0.5746 - acc: 0.7005
 56/102 [===============>..............] - ETA: 0s - loss: 0.5735 - acc: 0.7014
 75/102 [=====================>........] - ETA: 0s - loss: 0.5657 - acc: 0.7035
 94/102 [==========================>...] - ETA: 0s - loss: 0.5624 - acc: 0.7074
102/102 [==============================] - 0s 3ms/step - loss: 0.5579 - acc: 0.7134

102/102 [==============================] - 1s 5ms/step - loss: 0.5579 - acc: 0.7134 - val_loss: 0.5157 - val_acc: 0.7584
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.6122 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5818 - acc: 0.7035
 37/102 [=========>....................] - ETA: 0s - loss: 0.5562 - acc: 0.7076
 56/102 [===============>..............] - ETA: 0s - loss: 0.5434 - acc: 0.7175
 74/102 [====================>.........] - ETA: 0s - loss: 0.5439 - acc: 0.7254
 92/102 [==========================>...] - ETA: 0s - loss: 0.5461 - acc: 0.7270
102/102 [==============================] - 0s 3ms/step - loss: 0.5462 - acc: 0.7256

102/102 [==============================] - 1s 5ms/step - loss: 0.5462 - acc: 0.7256 - val_loss: 0.5165 - val_acc: 0.7531
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.7304 - acc: 0.6400
 19/102 [====>.........................] - ETA: 0s - loss: 0.5433 - acc: 0.7379
 30/102 [=======>......................] - ETA: 0s - loss: 0.5329 - acc: 0.7427
 48/102 [=============>................] - ETA: 0s - loss: 0.5304 - acc: 0.7371
 55/102 [===============>..............] - ETA: 0s - loss: 0.5293 - acc: 0.7382
 73/102 [====================>.........] - ETA: 0s - loss: 0.5273 - acc: 0.7405
 89/102 [=========================>....] - ETA: 0s - loss: 0.5340 - acc: 0.7373
102/102 [==============================] - 0s 3ms/step - loss: 0.5311 - acc: 0.7392

102/102 [==============================] - 1s 6ms/step - loss: 0.5311 - acc: 0.7392 - val_loss: 0.5007 - val_acc: 0.7620
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5272 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5263 - acc: 0.7278
 37/102 [=========>....................] - ETA: 0s - loss: 0.5310 - acc: 0.7427
 55/102 [===============>..............] - ETA: 0s - loss: 0.5388 - acc: 0.7313
 73/102 [====================>.........] - ETA: 0s - loss: 0.5278 - acc: 0.7359
 91/102 [=========================>....] - ETA: 0s - loss: 0.5248 - acc: 0.7407
102/102 [==============================] - 0s 3ms/step - loss: 0.5233 - acc: 0.7422

102/102 [==============================] - 1s 5ms/step - loss: 0.5233 - acc: 0.7422 - val_loss: 0.5055 - val_acc: 0.7496
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.4950 - acc: 0.6600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5097 - acc: 0.7318
 36/102 [=========>....................] - ETA: 0s - loss: 0.5046 - acc: 0.7444
 50/102 [=============>................] - ETA: 0s - loss: 0.5074 - acc: 0.7448
 62/102 [=================>............] - ETA: 0s - loss: 0.5169 - acc: 0.7397
 79/102 [======================>.......] - ETA: 0s - loss: 0.5239 - acc: 0.7352
 97/102 [===========================>..] - ETA: 0s - loss: 0.5203 - acc: 0.7373
102/102 [==============================] - 0s 3ms/step - loss: 0.5208 - acc: 0.7363

102/102 [==============================] - 1s 6ms/step - loss: 0.5208 - acc: 0.7363 - val_loss: 0.4985 - val_acc: 0.7549
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.8123 - acc: 0.6000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5240 - acc: 0.7344
 37/102 [=========>....................] - ETA: 0s - loss: 0.5277 - acc: 0.7308
 55/102 [===============>..............] - ETA: 0s - loss: 0.5229 - acc: 0.7204
 73/102 [====================>.........] - ETA: 0s - loss: 0.5139 - acc: 0.7279
 91/102 [=========================>....] - ETA: 0s - loss: 0.5129 - acc: 0.7301
102/102 [==============================] - 0s 3ms/step - loss: 0.5090 - acc: 0.7325

102/102 [==============================] - 1s 5ms/step - loss: 0.5090 - acc: 0.7325 - val_loss: 0.4887 - val_acc: 0.7620
Epoch 9/30

  1/102 [..............................] - ETA: 1s - loss: 0.7067 - acc: 0.6600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5480 - acc: 0.7422
 37/102 [=========>....................] - ETA: 0s - loss: 0.5275 - acc: 0.7297
 46/102 [============>.................] - ETA: 0s - loss: 0.5250 - acc: 0.7361
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 79/102 [======================>.......] - ETA: 0s - loss: 0.5220 - acc: 0.7334
 94/102 [==========================>...] - ETA: 0s - loss: 0.5169 - acc: 0.7377
102/102 [==============================] - 0s 3ms/step - loss: 0.5162 - acc: 0.7406

102/102 [==============================] - 1s 6ms/step - loss: 0.5162 - acc: 0.7406 - val_loss: 0.4978 - val_acc: 0.7513
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.4754 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5055 - acc: 0.7522
 37/102 [=========>....................] - ETA: 0s - loss: 0.5019 - acc: 0.7405
 50/102 [=============>................] - ETA: 0s - loss: 0.4955 - acc: 0.7484
 61/102 [================>.............] - ETA: 0s - loss: 0.5015 - acc: 0.7403
 75/102 [=====================>........] - ETA: 0s - loss: 0.5082 - acc: 0.7389
 94/102 [==========================>...] - ETA: 0s - loss: 0.5036 - acc: 0.7453
102/102 [==============================] - 0s 4ms/step - loss: 0.5032 - acc: 0.7459

102/102 [==============================] - 1s 7ms/step - loss: 0.5032 - acc: 0.7459 - val_loss: 0.4946 - val_acc: 0.7602
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.4519 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5001 - acc: 0.7500
 38/102 [==========>...................] - ETA: 0s - loss: 0.4995 - acc: 0.7521
 56/102 [===============>..............] - ETA: 0s - loss: 0.5026 - acc: 0.7386
 74/102 [====================>.........] - ETA: 0s - loss: 0.4940 - acc: 0.7424
 93/102 [==========================>...] - ETA: 0s - loss: 0.4954 - acc: 0.7409
102/102 [==============================] - 0s 3ms/step - loss: 0.4971 - acc: 0.7396

102/102 [==============================] - 1s 5ms/step - loss: 0.4971 - acc: 0.7396 - val_loss: 0.4885 - val_acc: 0.7620
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5020 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5026 - acc: 0.7433
 27/102 [======>.......................] - ETA: 0s - loss: 0.4919 - acc: 0.7415
 45/102 [============>.................] - ETA: 0s - loss: 0.5102 - acc: 0.7431
 63/102 [=================>............] - ETA: 0s - loss: 0.5022 - acc: 0.7441
 77/102 [=====================>........] - ETA: 0s - loss: 0.5097 - acc: 0.7382
 93/102 [==========================>...] - ETA: 0s - loss: 0.5050 - acc: 0.7357
102/102 [==============================] - 0s 3ms/step - loss: 0.5028 - acc: 0.7370

102/102 [==============================] - 1s 6ms/step - loss: 0.5028 - acc: 0.7370 - val_loss: 0.4850 - val_acc: 0.7673
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.7898 - acc: 0.5600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4990 - acc: 0.7344
 35/102 [=========>....................] - ETA: 0s - loss: 0.5096 - acc: 0.7337
 53/102 [==============>...............] - ETA: 0s - loss: 0.5085 - acc: 0.7355
 70/102 [===================>..........] - ETA: 0s - loss: 0.5023 - acc: 0.7409
 88/102 [========================>.....] - ETA: 0s - loss: 0.5005 - acc: 0.7402
102/102 [==============================] - 0s 3ms/step - loss: 0.4990 - acc: 0.7431

102/102 [==============================] - 0s 5ms/step - loss: 0.4990 - acc: 0.7431 - val_loss: 0.4886 - val_acc: 0.7673
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.3914 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4920 - acc: 0.7459
 26/102 [======>.......................] - ETA: 0s - loss: 0.4974 - acc: 0.7569
 31/102 [========>.....................] - ETA: 0s - loss: 0.5050 - acc: 0.7503
 48/102 [=============>................] - ETA: 0s - loss: 0.5044 - acc: 0.7458
 66/102 [==================>...........] - ETA: 0s - loss: 0.4936 - acc: 0.7458
 84/102 [=======================>......] - ETA: 0s - loss: 0.4844 - acc: 0.7486
102/102 [==============================] - 0s 4ms/step - loss: 0.4954 - acc: 0.7461

102/102 [==============================] - 1s 6ms/step - loss: 0.4954 - acc: 0.7461 - val_loss: 0.4863 - val_acc: 0.7673
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.4785 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4917 - acc: 0.7389
 37/102 [=========>....................] - ETA: 0s - loss: 0.4963 - acc: 0.7449
 55/102 [===============>..............] - ETA: 0s - loss: 0.4986 - acc: 0.7444
 72/102 [====================>.........] - ETA: 0s - loss: 0.4953 - acc: 0.7456
 90/102 [=========================>....] - ETA: 0s - loss: 0.4932 - acc: 0.7418
102/102 [==============================] - 0s 3ms/step - loss: 0.4947 - acc: 0.7461

102/102 [==============================] - 1s 6ms/step - loss: 0.4947 - acc: 0.7461 - val_loss: 0.4815 - val_acc: 0.7673
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.6056 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4973 - acc: 0.7576
 36/102 [=========>....................] - ETA: 0s - loss: 0.5035 - acc: 0.7511
 54/102 [==============>...............] - ETA: 0s - loss: 0.5063 - acc: 0.7415
 72/102 [====================>.........] - ETA: 0s - loss: 0.4990 - acc: 0.7400
 91/102 [=========================>....] - ETA: 0s - loss: 0.4959 - acc: 0.7426
102/102 [==============================] - 0s 3ms/step - loss: 0.4917 - acc: 0.7445

102/102 [==============================] - 1s 5ms/step - loss: 0.4917 - acc: 0.7445 - val_loss: 0.4856 - val_acc: 0.7655
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.3406 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4685 - acc: 0.7656
 35/102 [=========>....................] - ETA: 0s - loss: 0.5015 - acc: 0.7469
 54/102 [==============>...............] - ETA: 0s - loss: 0.4948 - acc: 0.7493
 70/102 [===================>..........] - ETA: 0s - loss: 0.4891 - acc: 0.7497
 78/102 [=====================>........] - ETA: 0s - loss: 0.4874 - acc: 0.7515
 95/102 [==========================>...] - ETA: 0s - loss: 0.4991 - acc: 0.7509
101/102 [============================>.] - ETA: 0s - loss: 0.5012 - acc: 0.7499
102/102 [==============================] - 0s 4ms/step - loss: 0.5007 - acc: 0.7500

102/102 [==============================] - 1s 6ms/step - loss: 0.5007 - acc: 0.7500 - val_loss: 0.5002 - val_acc: 0.7567
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4701 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4931 - acc: 0.7430

102/102 [==============================] - 1s 6ms/step - loss: 0.4931 - acc: 0.7430 - val_loss: 0.5040 - val_acc: 0.7513
Epoch 19/30

  1/102 [..............................] - ETA: 0s - loss: 0.4906 - acc: 0.8400
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102/102 [==============================] - 0s 3ms/step - loss: 0.4931 - acc: 0.7526

102/102 [==============================] - 1s 6ms/step - loss: 0.4931 - acc: 0.7526 - val_loss: 0.4967 - val_acc: 0.7638
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.5309 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4933 - acc: 0.7447

102/102 [==============================] - 1s 5ms/step - loss: 0.4933 - acc: 0.7447 - val_loss: 0.4944 - val_acc: 0.7531
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.5766 - acc: 0.7400
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 92/102 [==========================>...] - ETA: 0s - loss: 0.4881 - acc: 0.7461
102/102 [==============================] - 0s 3ms/step - loss: 0.4874 - acc: 0.7461

102/102 [==============================] - 1s 5ms/step - loss: 0.4874 - acc: 0.7461 - val_loss: 0.4854 - val_acc: 0.7531
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.3982 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4954 - acc: 0.7437

102/102 [==============================] - 1s 6ms/step - loss: 0.4954 - acc: 0.7437 - val_loss: 0.4795 - val_acc: 0.7691
Epoch 23/30

  1/102 [..............................] - ETA: 0s - loss: 0.5612 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4970 - acc: 0.7463

102/102 [==============================] - 0s 5ms/step - loss: 0.4970 - acc: 0.7463 - val_loss: 0.4988 - val_acc: 0.7584
Epoch 24/30

  1/102 [..............................] - ETA: 0s - loss: 0.5275 - acc: 0.7200
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102/102 [==============================] - 0s 4ms/step - loss: 0.4969 - acc: 0.7500

102/102 [==============================] - 1s 6ms/step - loss: 0.4969 - acc: 0.7500 - val_loss: 0.5012 - val_acc: 0.7584
Epoch 25/30

  1/102 [..............................] - ETA: 0s - loss: 0.5892 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4871 - acc: 0.7530

102/102 [==============================] - 1s 5ms/step - loss: 0.4871 - acc: 0.7530 - val_loss: 0.5022 - val_acc: 0.7549
Epoch 26/30

  1/102 [..............................] - ETA: 0s - loss: 0.4690 - acc: 0.7400
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101/102 [============================>.] - ETA: 0s - loss: 0.4882 - acc: 0.7556
102/102 [==============================] - 0s 4ms/step - loss: 0.4876 - acc: 0.7556

102/102 [==============================] - 1s 6ms/step - loss: 0.4876 - acc: 0.7556 - val_loss: 0.4952 - val_acc: 0.7584
Epoch 27/30

  1/102 [..............................] - ETA: 0s - loss: 0.4306 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4585 - acc: 0.7878
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102/102 [==============================] - 0s 3ms/step - loss: 0.4891 - acc: 0.7518

102/102 [==============================] - 1s 7ms/step - loss: 0.4891 - acc: 0.7518 - val_loss: 0.5001 - val_acc: 0.7620
Epoch 28/30

  1/102 [..............................] - ETA: 0s - loss: 0.3721 - acc: 0.8200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4920 - acc: 0.7444
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102/102 [==============================] - 0s 3ms/step - loss: 0.4901 - acc: 0.7512

102/102 [==============================] - 1s 5ms/step - loss: 0.4901 - acc: 0.7512 - val_loss: 0.5098 - val_acc: 0.7478
Epoch 29/30

  1/102 [..............................] - ETA: 0s - loss: 0.5847 - acc: 0.7600
 19/102 [====>.........................] - ETA: 0s - loss: 0.4751 - acc: 0.7579
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102/102 [==============================] - 0s 4ms/step - loss: 0.4852 - acc: 0.7556

102/102 [==============================] - 1s 6ms/step - loss: 0.4852 - acc: 0.7556 - val_loss: 0.5007 - val_acc: 0.7513
Epoch 30/30

  1/102 [..............................] - ETA: 0s - loss: 0.4192 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4921 - acc: 0.7556
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102/102 [==============================] - 0s 3ms/step - loss: 0.4862 - acc: 0.7621

102/102 [==============================] - 1s 5ms/step - loss: 0.4862 - acc: 0.7621 - val_loss: 0.5093 - val_acc: 0.7460


Run completed: runs/2024-05-06T19-34-22Z

Training run 3/52 (flags = list(32, 64, 0.001, 20, 30, "relu", "sigmoid", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-34-41Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 4:56 - loss: 0.7307 - acc: 0.3500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 16/254 [>.............................] - ETA: 0s - loss: 0.8165 - acc: 0.4281  
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253/254 [============================>.] - ETA: 0s - loss: 0.6506 - acc: 0.6188
254/254 [==============================] - 2s 3ms/step - loss: 0.6505 - acc: 0.6192

254/254 [==============================] - 3s 6ms/step - loss: 0.6505 - acc: 0.6192 - val_loss: 0.5049 - val_acc: 0.7602
Epoch 2/30

  1/254 [..............................] - ETA: 0s - loss: 0.3107 - acc: 0.8500
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128/254 [==============>...............] - ETA: 0s - loss: 0.5687 - acc: 0.7168
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244/254 [===========================>..] - ETA: 0s - loss: 0.5560 - acc: 0.7221
254/254 [==============================] - 1s 4ms/step - loss: 0.5523 - acc: 0.7246

254/254 [==============================] - 1s 5ms/step - loss: 0.5523 - acc: 0.7246 - val_loss: 0.4861 - val_acc: 0.7691
Epoch 3/30

  1/254 [..............................] - ETA: 0s - loss: 0.6341 - acc: 0.7000
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244/254 [===========================>..] - ETA: 0s - loss: 0.5365 - acc: 0.7338
254/254 [==============================] - 1s 3ms/step - loss: 0.5359 - acc: 0.7349

254/254 [==============================] - 1s 4ms/step - loss: 0.5359 - acc: 0.7349 - val_loss: 0.5016 - val_acc: 0.7496
Epoch 4/30

  1/254 [..............................] - ETA: 0s - loss: 0.7011 - acc: 0.6000
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253/254 [============================>.] - ETA: 0s - loss: 0.5135 - acc: 0.7443
254/254 [==============================] - 1s 3ms/step - loss: 0.5137 - acc: 0.7443

254/254 [==============================] - 1s 4ms/step - loss: 0.5137 - acc: 0.7443 - val_loss: 0.4942 - val_acc: 0.7567
Epoch 5/30

  1/254 [..............................] - ETA: 0s - loss: 0.3441 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5125 - acc: 0.7463

254/254 [==============================] - 1s 4ms/step - loss: 0.5125 - acc: 0.7463 - val_loss: 0.5125 - val_acc: 0.7442
Epoch 6/30

  1/254 [..............................] - ETA: 0s - loss: 0.5026 - acc: 0.8500
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254/254 [==============================] - 1s 4ms/step - loss: 0.5038 - acc: 0.7495

254/254 [==============================] - 1s 4ms/step - loss: 0.5038 - acc: 0.7495 - val_loss: 0.4665 - val_acc: 0.7726
Epoch 7/30

  1/254 [..............................] - ETA: 1s - loss: 0.5795 - acc: 0.7500
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237/254 [==========================>...] - ETA: 0s - loss: 0.5094 - acc: 0.7411
254/254 [==============================] - 1s 3ms/step - loss: 0.5072 - acc: 0.7424

254/254 [==============================] - 1s 4ms/step - loss: 0.5072 - acc: 0.7424 - val_loss: 0.4756 - val_acc: 0.7655
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5047 - acc: 0.7473

254/254 [==============================] - 1s 4ms/step - loss: 0.5047 - acc: 0.7473 - val_loss: 0.4826 - val_acc: 0.7638
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5019 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.5019 - acc: 0.7493 - val_loss: 0.4904 - val_acc: 0.7602
Epoch 10/30

  1/254 [..............................] - ETA: 1s - loss: 0.3271 - acc: 0.7500
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254/254 [==============================] - 1s 4ms/step - loss: 0.5045 - acc: 0.7457

254/254 [==============================] - 1s 5ms/step - loss: 0.5045 - acc: 0.7457 - val_loss: 0.4855 - val_acc: 0.7638
Epoch 11/30

  1/254 [..............................] - ETA: 0s - loss: 0.3815 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5021 - acc: 0.7471

254/254 [==============================] - 1s 4ms/step - loss: 0.5021 - acc: 0.7471 - val_loss: 0.5099 - val_acc: 0.7496
Epoch 12/30

  1/254 [..............................] - ETA: 1s - loss: 0.4686 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5029 - acc: 0.7439

254/254 [==============================] - 1s 4ms/step - loss: 0.5029 - acc: 0.7439 - val_loss: 0.4787 - val_acc: 0.7584
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4989 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.4989 - acc: 0.7493 - val_loss: 0.4742 - val_acc: 0.7655
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4978 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.4978 - acc: 0.7493 - val_loss: 0.4716 - val_acc: 0.7673
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4924 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4924 - acc: 0.7542 - val_loss: 0.4890 - val_acc: 0.7496
Epoch 16/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4936 - acc: 0.7437

254/254 [==============================] - 1s 4ms/step - loss: 0.4936 - acc: 0.7437 - val_loss: 0.4740 - val_acc: 0.7620


Run completed: runs/2024-05-06T19-34-41Z

Training run 4/52 (flags = list(64, 16, 0.001, 50, 50, "relu", "sigmoid", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-35-00Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:23 - loss: 0.5790 - acc: 0.7400

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0678s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0678s). Check your callbacks.


 19/102 [====>.........................] - ETA: 0s - loss: 0.7135 - acc: 0.6800  
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102/102 [==============================] - 2s 3ms/step - loss: 0.6339 - acc: 0.6682

102/102 [==============================] - 3s 12ms/step - loss: 0.6339 - acc: 0.6682 - val_loss: 0.5138 - val_acc: 0.7691
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.5888 - acc: 0.7600
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102/102 [==============================] - 0s 4ms/step - loss: 0.5301 - acc: 0.7372

102/102 [==============================] - 1s 6ms/step - loss: 0.5301 - acc: 0.7372 - val_loss: 0.4929 - val_acc: 0.7673
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.5630 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.5144 - acc: 0.7435

102/102 [==============================] - 1s 6ms/step - loss: 0.5144 - acc: 0.7435 - val_loss: 0.4835 - val_acc: 0.7655
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.6220 - acc: 0.8000
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102/102 [==============================] - 0s 4ms/step - loss: 0.5108 - acc: 0.7453

102/102 [==============================] - 1s 7ms/step - loss: 0.5108 - acc: 0.7453 - val_loss: 0.4638 - val_acc: 0.7709
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.5320 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.5024 - acc: 0.7479

102/102 [==============================] - 1s 5ms/step - loss: 0.5024 - acc: 0.7479 - val_loss: 0.4607 - val_acc: 0.7726
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.5587 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.5040 - acc: 0.7473

102/102 [==============================] - 1s 6ms/step - loss: 0.5040 - acc: 0.7473 - val_loss: 0.4818 - val_acc: 0.7638
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.4991 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.4973 - acc: 0.7540

102/102 [==============================] - 1s 6ms/step - loss: 0.4973 - acc: 0.7540 - val_loss: 0.4899 - val_acc: 0.7513
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.5394 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.4973 - acc: 0.7449

102/102 [==============================] - 1s 5ms/step - loss: 0.4973 - acc: 0.7449 - val_loss: 0.4919 - val_acc: 0.7478
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.4919 - acc: 0.7200
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100/102 [============================>.] - ETA: 0s - loss: 0.4948 - acc: 0.7544
102/102 [==============================] - 0s 5ms/step - loss: 0.4946 - acc: 0.7540

102/102 [==============================] - 1s 8ms/step - loss: 0.4946 - acc: 0.7540 - val_loss: 0.4943 - val_acc: 0.7496
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4555 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5054 - acc: 0.7294
 33/102 [========>.....................] - ETA: 0s - loss: 0.5028 - acc: 0.7370
 49/102 [=============>................] - ETA: 0s - loss: 0.5016 - acc: 0.7437
 65/102 [==================>...........] - ETA: 0s - loss: 0.4935 - acc: 0.7483
 82/102 [=======================>......] - ETA: 0s - loss: 0.4915 - acc: 0.7483
 98/102 [===========================>..] - ETA: 0s - loss: 0.4957 - acc: 0.7494
102/102 [==============================] - 0s 3ms/step - loss: 0.4942 - acc: 0.7514

102/102 [==============================] - 1s 7ms/step - loss: 0.4942 - acc: 0.7514 - val_loss: 0.4918 - val_acc: 0.7496
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.3508 - acc: 0.9400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5030 - acc: 0.7329
 35/102 [=========>....................] - ETA: 0s - loss: 0.4859 - acc: 0.7480
 54/102 [==============>...............] - ETA: 0s - loss: 0.4850 - acc: 0.7544
 71/102 [===================>..........] - ETA: 0s - loss: 0.4859 - acc: 0.7538
 89/102 [=========================>....] - ETA: 0s - loss: 0.4863 - acc: 0.7499
102/102 [==============================] - 0s 3ms/step - loss: 0.4913 - acc: 0.7497

102/102 [==============================] - 1s 5ms/step - loss: 0.4913 - acc: 0.7497 - val_loss: 0.4848 - val_acc: 0.7567
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.4797 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5243 - acc: 0.7282
 34/102 [=========>....................] - ETA: 0s - loss: 0.5049 - acc: 0.7400
 50/102 [=============>................] - ETA: 0s - loss: 0.4945 - acc: 0.7504
 57/102 [===============>..............] - ETA: 0s - loss: 0.4954 - acc: 0.7453
 62/102 [=================>............] - ETA: 0s - loss: 0.4912 - acc: 0.7497
 77/102 [=====================>........] - ETA: 0s - loss: 0.4871 - acc: 0.7483
 86/102 [========================>.....] - ETA: 0s - loss: 0.4877 - acc: 0.7512
102/102 [==============================] - 0s 4ms/step - loss: 0.4868 - acc: 0.7518

102/102 [==============================] - 1s 7ms/step - loss: 0.4868 - acc: 0.7518 - val_loss: 0.4886 - val_acc: 0.7531
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.5809 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5067 - acc: 0.7256
 36/102 [=========>....................] - ETA: 0s - loss: 0.4884 - acc: 0.7444
 54/102 [==============>...............] - ETA: 0s - loss: 0.4904 - acc: 0.7396
 71/102 [===================>..........] - ETA: 0s - loss: 0.4903 - acc: 0.7425
 90/102 [=========================>....] - ETA: 0s - loss: 0.4882 - acc: 0.7462
102/102 [==============================] - 0s 3ms/step - loss: 0.4874 - acc: 0.7467

102/102 [==============================] - 1s 5ms/step - loss: 0.4874 - acc: 0.7467 - val_loss: 0.4880 - val_acc: 0.7478
Epoch 14/50

  1/102 [..............................] - ETA: 1s - loss: 0.4191 - acc: 0.8000
 12/102 [==>...........................] - ETA: 0s - loss: 0.4856 - acc: 0.7783
 22/102 [=====>........................] - ETA: 0s - loss: 0.4848 - acc: 0.7745
 37/102 [=========>....................] - ETA: 0s - loss: 0.4952 - acc: 0.7600
 49/102 [=============>................] - ETA: 0s - loss: 0.4861 - acc: 0.7596
 67/102 [==================>...........] - ETA: 0s - loss: 0.4934 - acc: 0.7525
 84/102 [=======================>......] - ETA: 0s - loss: 0.4847 - acc: 0.7562
101/102 [============================>.] - ETA: 0s - loss: 0.4861 - acc: 0.7547
102/102 [==============================] - 0s 4ms/step - loss: 0.4864 - acc: 0.7550

102/102 [==============================] - 1s 6ms/step - loss: 0.4864 - acc: 0.7550 - val_loss: 0.4838 - val_acc: 0.7513
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.6883 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5151 - acc: 0.7378
 36/102 [=========>....................] - ETA: 0s - loss: 0.5075 - acc: 0.7383
 53/102 [==============>...............] - ETA: 0s - loss: 0.4969 - acc: 0.7453
 71/102 [===================>..........] - ETA: 0s - loss: 0.4881 - acc: 0.7527
 89/102 [=========================>....] - ETA: 0s - loss: 0.4955 - acc: 0.7524
102/102 [==============================] - 0s 3ms/step - loss: 0.4924 - acc: 0.7508

102/102 [==============================] - 1s 6ms/step - loss: 0.4924 - acc: 0.7508 - val_loss: 0.4878 - val_acc: 0.7513


Run completed: runs/2024-05-06T19-35-00Z

Training run 5/52 (flags = list(64, 16, 0.01, 50, 30, "relu", "sigmoid", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-35-12Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:02 - loss: 0.8643 - acc: 0.2600

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


 14/102 [===>..........................] - ETA: 0s - loss: 0.7732 - acc: 0.3814  
 32/102 [========>.....................] - ETA: 0s - loss: 0.7336 - acc: 0.4481
 46/102 [============>.................] - ETA: 0s - loss: 0.7087 - acc: 0.4909
 63/102 [=================>............] - ETA: 0s - loss: 0.6840 - acc: 0.5200
 81/102 [======================>.......] - ETA: 0s - loss: 0.6699 - acc: 0.5506
 98/102 [===========================>..] - ETA: 0s - loss: 0.6545 - acc: 0.5710
102/102 [==============================] - 2s 4ms/step - loss: 0.6527 - acc: 0.5732

102/102 [==============================] - 2s 12ms/step - loss: 0.6527 - acc: 0.5732 - val_loss: 0.5376 - val_acc: 0.7496
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.4701 - acc: 0.7200
 13/102 [==>...........................] - ETA: 0s - loss: 0.5573 - acc: 0.7077
 30/102 [=======>......................] - ETA: 0s - loss: 0.5613 - acc: 0.7000
 44/102 [===========>..................] - ETA: 0s - loss: 0.5643 - acc: 0.6959
 50/102 [=============>................] - ETA: 0s - loss: 0.5620 - acc: 0.6988
 66/102 [==================>...........] - ETA: 0s - loss: 0.5587 - acc: 0.7000
 78/102 [=====================>........] - ETA: 0s - loss: 0.5555 - acc: 0.7018
 96/102 [===========================>..] - ETA: 0s - loss: 0.5528 - acc: 0.7085
102/102 [==============================] - 0s 4ms/step - loss: 0.5505 - acc: 0.7077

102/102 [==============================] - 1s 6ms/step - loss: 0.5505 - acc: 0.7077 - val_loss: 0.5115 - val_acc: 0.7638
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.5754 - acc: 0.7200
 15/102 [===>..........................] - ETA: 0s - loss: 0.5383 - acc: 0.7080
 31/102 [========>.....................] - ETA: 0s - loss: 0.5399 - acc: 0.7090
 49/102 [=============>................] - ETA: 0s - loss: 0.5371 - acc: 0.7184
 67/102 [==================>...........] - ETA: 0s - loss: 0.5280 - acc: 0.7266
 83/102 [=======================>......] - ETA: 0s - loss: 0.5267 - acc: 0.7292
101/102 [============================>.] - ETA: 0s - loss: 0.5267 - acc: 0.7313
102/102 [==============================] - 0s 3ms/step - loss: 0.5262 - acc: 0.7317

102/102 [==============================] - 1s 6ms/step - loss: 0.5262 - acc: 0.7317 - val_loss: 0.4984 - val_acc: 0.7673
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.4735 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5282 - acc: 0.7459
 36/102 [=========>....................] - ETA: 0s - loss: 0.5201 - acc: 0.7494
 45/102 [============>.................] - ETA: 0s - loss: 0.5228 - acc: 0.7480
 53/102 [==============>...............] - ETA: 0s - loss: 0.5200 - acc: 0.7506
 71/102 [===================>..........] - ETA: 0s - loss: 0.5230 - acc: 0.7490
 81/102 [======================>.......] - ETA: 0s - loss: 0.5218 - acc: 0.7472
 98/102 [===========================>..] - ETA: 0s - loss: 0.5193 - acc: 0.7484
102/102 [==============================] - 0s 4ms/step - loss: 0.5194 - acc: 0.7477

102/102 [==============================] - 1s 6ms/step - loss: 0.5194 - acc: 0.7477 - val_loss: 0.4994 - val_acc: 0.7638
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.5993 - acc: 0.6600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5381 - acc: 0.7271
 36/102 [=========>....................] - ETA: 0s - loss: 0.5334 - acc: 0.7300
 54/102 [==============>...............] - ETA: 0s - loss: 0.5297 - acc: 0.7389
 71/102 [===================>..........] - ETA: 0s - loss: 0.5216 - acc: 0.7456
 89/102 [=========================>....] - ETA: 0s - loss: 0.5188 - acc: 0.7443
102/102 [==============================] - 0s 3ms/step - loss: 0.5171 - acc: 0.7469

102/102 [==============================] - 1s 5ms/step - loss: 0.5171 - acc: 0.7469 - val_loss: 0.4937 - val_acc: 0.7673
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5021 - acc: 0.8200
 13/102 [==>...........................] - ETA: 0s - loss: 0.4778 - acc: 0.7631
 23/102 [=====>........................] - ETA: 0s - loss: 0.5039 - acc: 0.7522
 37/102 [=========>....................] - ETA: 0s - loss: 0.5020 - acc: 0.7530
 51/102 [==============>...............] - ETA: 0s - loss: 0.5094 - acc: 0.7463
 67/102 [==================>...........] - ETA: 0s - loss: 0.5043 - acc: 0.7549
 85/102 [========================>.....] - ETA: 0s - loss: 0.5063 - acc: 0.7527
101/102 [============================>.] - ETA: 0s - loss: 0.5100 - acc: 0.7527
102/102 [==============================] - 0s 4ms/step - loss: 0.5099 - acc: 0.7526

102/102 [==============================] - 1s 6ms/step - loss: 0.5099 - acc: 0.7526 - val_loss: 0.4928 - val_acc: 0.7620
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.4190 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4885 - acc: 0.7556
 37/102 [=========>....................] - ETA: 0s - loss: 0.4824 - acc: 0.7686
 54/102 [==============>...............] - ETA: 0s - loss: 0.4999 - acc: 0.7600
 66/102 [==================>...........] - ETA: 0s - loss: 0.5033 - acc: 0.7600
 84/102 [=======================>......] - ETA: 0s - loss: 0.5027 - acc: 0.7581
 97/102 [===========================>..] - ETA: 0s - loss: 0.4986 - acc: 0.7575
102/102 [==============================] - 0s 4ms/step - loss: 0.5002 - acc: 0.7568

102/102 [==============================] - 1s 7ms/step - loss: 0.5002 - acc: 0.7568 - val_loss: 0.4816 - val_acc: 0.7709
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.4404 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5225 - acc: 0.7529
 35/102 [=========>....................] - ETA: 0s - loss: 0.5135 - acc: 0.7577
 53/102 [==============>...............] - ETA: 0s - loss: 0.5085 - acc: 0.7543
 69/102 [===================>..........] - ETA: 0s - loss: 0.5080 - acc: 0.7484
 87/102 [========================>.....] - ETA: 0s - loss: 0.5052 - acc: 0.7529
102/102 [==============================] - 0s 3ms/step - loss: 0.5036 - acc: 0.7542

102/102 [==============================] - 1s 5ms/step - loss: 0.5036 - acc: 0.7542 - val_loss: 0.4771 - val_acc: 0.7709
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.5473 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5022 - acc: 0.7656
 29/102 [=======>......................] - ETA: 0s - loss: 0.4790 - acc: 0.7807
 39/102 [==========>...................] - ETA: 0s - loss: 0.4888 - acc: 0.7713
 53/102 [==============>...............] - ETA: 0s - loss: 0.4870 - acc: 0.7694
 71/102 [===================>..........] - ETA: 0s - loss: 0.4985 - acc: 0.7634
 89/102 [=========================>....] - ETA: 0s - loss: 0.4989 - acc: 0.7600
102/102 [==============================] - 0s 3ms/step - loss: 0.5014 - acc: 0.7599

102/102 [==============================] - 1s 6ms/step - loss: 0.5014 - acc: 0.7599 - val_loss: 0.4840 - val_acc: 0.7655
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.5902 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5119 - acc: 0.7435
 35/102 [=========>....................] - ETA: 0s - loss: 0.4952 - acc: 0.7503
 53/102 [==============>...............] - ETA: 0s - loss: 0.5051 - acc: 0.7513
 71/102 [===================>..........] - ETA: 0s - loss: 0.5018 - acc: 0.7530
 88/102 [========================>.....] - ETA: 0s - loss: 0.5041 - acc: 0.7550
102/102 [==============================] - 0s 3ms/step - loss: 0.5026 - acc: 0.7558

102/102 [==============================] - 1s 5ms/step - loss: 0.5026 - acc: 0.7558 - val_loss: 0.4906 - val_acc: 0.7691
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5071 - acc: 0.6800
  9/102 [=>............................] - ETA: 0s - loss: 0.4864 - acc: 0.7622
 25/102 [======>.......................] - ETA: 0s - loss: 0.4994 - acc: 0.7664
 36/102 [=========>....................] - ETA: 0s - loss: 0.5065 - acc: 0.7583
 45/102 [============>.................] - ETA: 0s - loss: 0.5097 - acc: 0.7556
 62/102 [=================>............] - ETA: 0s - loss: 0.5060 - acc: 0.7555
 80/102 [======================>.......] - ETA: 0s - loss: 0.5026 - acc: 0.7552
 97/102 [===========================>..] - ETA: 0s - loss: 0.4990 - acc: 0.7553
102/102 [==============================] - 0s 4ms/step - loss: 0.4977 - acc: 0.7564

102/102 [==============================] - 1s 7ms/step - loss: 0.4977 - acc: 0.7564 - val_loss: 0.4733 - val_acc: 0.7744
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.4394 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4817 - acc: 0.7776
 34/102 [=========>....................] - ETA: 0s - loss: 0.4796 - acc: 0.7718
 52/102 [==============>...............] - ETA: 0s - loss: 0.4918 - acc: 0.7688
 69/102 [===================>..........] - ETA: 0s - loss: 0.4944 - acc: 0.7716
 77/102 [=====================>........] - ETA: 0s - loss: 0.4961 - acc: 0.7701
 94/102 [==========================>...] - ETA: 0s - loss: 0.5001 - acc: 0.7628
102/102 [==============================] - 0s 3ms/step - loss: 0.5003 - acc: 0.7607

102/102 [==============================] - 1s 6ms/step - loss: 0.5003 - acc: 0.7607 - val_loss: 0.4731 - val_acc: 0.7798
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.5228 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4967 - acc: 0.7894
 35/102 [=========>....................] - ETA: 0s - loss: 0.5078 - acc: 0.7651
 52/102 [==============>...............] - ETA: 0s - loss: 0.5010 - acc: 0.7658
 69/102 [===================>..........] - ETA: 0s - loss: 0.5007 - acc: 0.7664
 87/102 [========================>.....] - ETA: 0s - loss: 0.5013 - acc: 0.7637
102/102 [==============================] - 0s 3ms/step - loss: 0.5035 - acc: 0.7625

102/102 [==============================] - 1s 5ms/step - loss: 0.5035 - acc: 0.7625 - val_loss: 0.4805 - val_acc: 0.7744
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.5735 - acc: 0.6800
 16/102 [===>..........................] - ETA: 0s - loss: 0.4776 - acc: 0.7650
 25/102 [======>.......................] - ETA: 0s - loss: 0.4741 - acc: 0.7704
 35/102 [=========>....................] - ETA: 0s - loss: 0.4763 - acc: 0.7771
 53/102 [==============>...............] - ETA: 0s - loss: 0.4888 - acc: 0.7721
 70/102 [===================>..........] - ETA: 0s - loss: 0.4894 - acc: 0.7697
 82/102 [=======================>......] - ETA: 0s - loss: 0.4946 - acc: 0.7693
100/102 [============================>.] - ETA: 0s - loss: 0.5002 - acc: 0.7648
102/102 [==============================] - 0s 4ms/step - loss: 0.4993 - acc: 0.7642

102/102 [==============================] - 1s 7ms/step - loss: 0.4993 - acc: 0.7642 - val_loss: 0.4947 - val_acc: 0.7584
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5152 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4834 - acc: 0.7565
 32/102 [========>.....................] - ETA: 0s - loss: 0.4831 - acc: 0.7644
 49/102 [=============>................] - ETA: 0s - loss: 0.4970 - acc: 0.7641
 64/102 [=================>............] - ETA: 0s - loss: 0.4928 - acc: 0.7697
 82/102 [=======================>......] - ETA: 0s - loss: 0.4921 - acc: 0.7668
 99/102 [============================>.] - ETA: 0s - loss: 0.4922 - acc: 0.7667
102/102 [==============================] - 0s 3ms/step - loss: 0.4936 - acc: 0.7662

102/102 [==============================] - 1s 6ms/step - loss: 0.4936 - acc: 0.7662 - val_loss: 0.4709 - val_acc: 0.7780
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.4484 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4646 - acc: 0.7887
 34/102 [=========>....................] - ETA: 0s - loss: 0.4920 - acc: 0.7700
 50/102 [=============>................] - ETA: 0s - loss: 0.4852 - acc: 0.7780
 68/102 [===================>..........] - ETA: 0s - loss: 0.4856 - acc: 0.7697
 86/102 [========================>.....] - ETA: 0s - loss: 0.4898 - acc: 0.7702
102/102 [==============================] - 0s 3ms/step - loss: 0.4938 - acc: 0.7684

102/102 [==============================] - 1s 5ms/step - loss: 0.4938 - acc: 0.7684 - val_loss: 0.5026 - val_acc: 0.7584
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.3815 - acc: 0.8800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5093 - acc: 0.7500
 36/102 [=========>....................] - ETA: 0s - loss: 0.5029 - acc: 0.7489
 53/102 [==============>...............] - ETA: 0s - loss: 0.4943 - acc: 0.7532
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 80/102 [======================>.......] - ETA: 0s - loss: 0.4920 - acc: 0.7617
 94/102 [==========================>...] - ETA: 0s - loss: 0.4901 - acc: 0.7630
102/102 [==============================] - 0s 4ms/step - loss: 0.4919 - acc: 0.7625

102/102 [==============================] - 1s 6ms/step - loss: 0.4919 - acc: 0.7625 - val_loss: 0.4781 - val_acc: 0.7620
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.5520 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5078 - acc: 0.7822
 37/102 [=========>....................] - ETA: 0s - loss: 0.5131 - acc: 0.7557
 54/102 [==============>...............] - ETA: 0s - loss: 0.5107 - acc: 0.7489
 71/102 [===================>..........] - ETA: 0s - loss: 0.5001 - acc: 0.7510
 88/102 [========================>.....] - ETA: 0s - loss: 0.4956 - acc: 0.7620
102/102 [==============================] - 0s 3ms/step - loss: 0.4934 - acc: 0.7644

102/102 [==============================] - 1s 5ms/step - loss: 0.4934 - acc: 0.7644 - val_loss: 0.4677 - val_acc: 0.7691
Epoch 19/30

  1/102 [..............................] - ETA: 1s - loss: 0.6508 - acc: 0.6200
 15/102 [===>..........................] - ETA: 0s - loss: 0.4938 - acc: 0.7613
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 43/102 [===========>..................] - ETA: 0s - loss: 0.4888 - acc: 0.7749
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 93/102 [==========================>...] - ETA: 0s - loss: 0.4846 - acc: 0.7759
102/102 [==============================] - 0s 3ms/step - loss: 0.4906 - acc: 0.7709

102/102 [==============================] - 1s 6ms/step - loss: 0.4906 - acc: 0.7709 - val_loss: 0.4789 - val_acc: 0.7655
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.6009 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4920 - acc: 0.7765
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102/102 [==============================] - 0s 3ms/step - loss: 0.4876 - acc: 0.7690

102/102 [==============================] - 1s 6ms/step - loss: 0.4876 - acc: 0.7690 - val_loss: 0.4971 - val_acc: 0.7602
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.4640 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4921 - acc: 0.7684

102/102 [==============================] - 1s 7ms/step - loss: 0.4921 - acc: 0.7684 - val_loss: 0.4775 - val_acc: 0.7709
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.4320 - acc: 0.7000
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102/102 [==============================] - 0s 4ms/step - loss: 0.4923 - acc: 0.7694

102/102 [==============================] - 1s 6ms/step - loss: 0.4923 - acc: 0.7694 - val_loss: 0.4855 - val_acc: 0.7620
Epoch 23/30

  1/102 [..............................] - ETA: 0s - loss: 0.6020 - acc: 0.8000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4940 - acc: 0.7621

102/102 [==============================] - 1s 5ms/step - loss: 0.4940 - acc: 0.7621 - val_loss: 0.4797 - val_acc: 0.7673
Epoch 24/30

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102/102 [==============================] - 0s 4ms/step - loss: 0.4858 - acc: 0.7686

102/102 [==============================] - 1s 6ms/step - loss: 0.4858 - acc: 0.7686 - val_loss: 0.4930 - val_acc: 0.7567
Epoch 25/30

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102/102 [==============================] - 0s 3ms/step - loss: 0.4848 - acc: 0.7627

102/102 [==============================] - 1s 6ms/step - loss: 0.4848 - acc: 0.7627 - val_loss: 0.4749 - val_acc: 0.7726
Epoch 26/30

  1/102 [..............................] - ETA: 0s - loss: 0.3724 - acc: 0.8400
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102/102 [==============================] - 0s 3ms/step - loss: 0.4899 - acc: 0.7690

102/102 [==============================] - 1s 5ms/step - loss: 0.4899 - acc: 0.7690 - val_loss: 0.4777 - val_acc: 0.7709
Epoch 27/30

  1/102 [..............................] - ETA: 0s - loss: 0.5306 - acc: 0.7800
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102/102 [==============================] - 0s 4ms/step - loss: 0.4895 - acc: 0.7709

102/102 [==============================] - 1s 6ms/step - loss: 0.4895 - acc: 0.7709 - val_loss: 0.4888 - val_acc: 0.7584
Epoch 28/30

  1/102 [..............................] - ETA: 0s - loss: 0.6270 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4760 - acc: 0.7644
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102/102 [==============================] - 0s 3ms/step - loss: 0.4864 - acc: 0.7670

102/102 [==============================] - 1s 6ms/step - loss: 0.4864 - acc: 0.7670 - val_loss: 0.4956 - val_acc: 0.7620


Run completed: runs/2024-05-06T19-35-12Z

Training run 6/52 (flags = list(64, 16, 0.01, 20, 50, "relu", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-35-31Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:10 - loss: 0.9417 - acc: 0.2500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0298s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0298s). Check your callbacks.


 14/254 [>.............................] - ETA: 0s - loss: 0.6994 - acc: 0.5321  
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254/254 [==============================] - 2s 3ms/step - loss: 0.5377 - acc: 0.7240

254/254 [==============================] - 3s 6ms/step - loss: 0.5377 - acc: 0.7240 - val_loss: 0.4897 - val_acc: 0.7531
Epoch 2/50

  1/254 [..............................] - ETA: 0s - loss: 0.7334 - acc: 0.4500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4964 - acc: 0.7540

254/254 [==============================] - 1s 4ms/step - loss: 0.4964 - acc: 0.7540 - val_loss: 0.5017 - val_acc: 0.7425
Epoch 3/50

  1/254 [..............................] - ETA: 0s - loss: 0.2980 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4913 - acc: 0.7499

254/254 [==============================] - 1s 5ms/step - loss: 0.4913 - acc: 0.7499 - val_loss: 0.4911 - val_acc: 0.7531
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 0.4332 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4863 - acc: 0.7489

254/254 [==============================] - 1s 4ms/step - loss: 0.4863 - acc: 0.7489 - val_loss: 0.4921 - val_acc: 0.7496
Epoch 5/50

  1/254 [..............................] - ETA: 1s - loss: 0.4072 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4842 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4842 - acc: 0.7575 - val_loss: 0.4785 - val_acc: 0.7567
Epoch 6/50

  1/254 [..............................] - ETA: 1s - loss: 0.5973 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4802 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4802 - acc: 0.7524 - val_loss: 0.4735 - val_acc: 0.7726
Epoch 7/50

  1/254 [..............................] - ETA: 1s - loss: 0.4078 - acc: 0.8500
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254/254 [==============================] - 1s 4ms/step - loss: 0.4783 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4783 - acc: 0.7550 - val_loss: 0.4764 - val_acc: 0.7655
Epoch 8/50

  1/254 [..............................] - ETA: 2s - loss: 0.4141 - acc: 0.9000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4753 - acc: 0.7595

254/254 [==============================] - 1s 4ms/step - loss: 0.4753 - acc: 0.7595 - val_loss: 0.4958 - val_acc: 0.7531
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4727 - acc: 0.7540

254/254 [==============================] - 1s 4ms/step - loss: 0.4727 - acc: 0.7540 - val_loss: 0.4767 - val_acc: 0.7655
Epoch 10/50

  1/254 [..............................] - ETA: 3s - loss: 0.1915 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4701 - acc: 0.7605

254/254 [==============================] - 1s 4ms/step - loss: 0.4701 - acc: 0.7605 - val_loss: 0.5099 - val_acc: 0.7425
Epoch 11/50

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241/254 [===========================>..] - ETA: 0s - loss: 0.4653 - acc: 0.7604
254/254 [==============================] - 1s 3ms/step - loss: 0.4694 - acc: 0.7571

254/254 [==============================] - 1s 4ms/step - loss: 0.4694 - acc: 0.7571 - val_loss: 0.5021 - val_acc: 0.7371
Epoch 12/50

  1/254 [..............................] - ETA: 0s - loss: 0.6014 - acc: 0.7500
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241/254 [===========================>..] - ETA: 0s - loss: 0.4649 - acc: 0.7676
254/254 [==============================] - 1s 3ms/step - loss: 0.4660 - acc: 0.7678

254/254 [==============================] - 1s 4ms/step - loss: 0.4660 - acc: 0.7678 - val_loss: 0.5036 - val_acc: 0.7513
Epoch 13/50

  1/254 [..............................] - ETA: 1s - loss: 0.4021 - acc: 0.7000
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111/254 [============>.................] - ETA: 0s - loss: 0.4664 - acc: 0.7563
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254/254 [==============================] - 1s 3ms/step - loss: 0.4623 - acc: 0.7635

254/254 [==============================] - 1s 4ms/step - loss: 0.4623 - acc: 0.7635 - val_loss: 0.5025 - val_acc: 0.7496
Epoch 14/50

  1/254 [..............................] - ETA: 0s - loss: 0.4642 - acc: 0.6500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4605 - acc: 0.7625

254/254 [==============================] - 1s 4ms/step - loss: 0.4605 - acc: 0.7625 - val_loss: 0.4679 - val_acc: 0.7655
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4586 - acc: 0.7711

254/254 [==============================] - 1s 4ms/step - loss: 0.4586 - acc: 0.7711 - val_loss: 0.4901 - val_acc: 0.7638
Epoch 16/50

  1/254 [..............................] - ETA: 1s - loss: 0.7422 - acc: 0.6500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4567 - acc: 0.7717

254/254 [==============================] - 1s 4ms/step - loss: 0.4567 - acc: 0.7717 - val_loss: 0.5126 - val_acc: 0.7531
Epoch 17/50

  1/254 [..............................] - ETA: 0s - loss: 0.6290 - acc: 0.6000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4562 - acc: 0.7733

254/254 [==============================] - 1s 4ms/step - loss: 0.4562 - acc: 0.7733 - val_loss: 0.5135 - val_acc: 0.7460
Epoch 18/50

  1/254 [..............................] - ETA: 0s - loss: 0.3678 - acc: 0.9000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4520 - acc: 0.7731

254/254 [==============================] - 1s 4ms/step - loss: 0.4520 - acc: 0.7731 - val_loss: 0.4829 - val_acc: 0.7673
Epoch 19/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4579 - acc: 0.7617

254/254 [==============================] - 1s 4ms/step - loss: 0.4579 - acc: 0.7617 - val_loss: 0.4904 - val_acc: 0.7691
Epoch 20/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4504 - acc: 0.7709

254/254 [==============================] - 1s 4ms/step - loss: 0.4504 - acc: 0.7709 - val_loss: 0.4931 - val_acc: 0.7620
Epoch 21/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4477 - acc: 0.7698

254/254 [==============================] - 1s 4ms/step - loss: 0.4477 - acc: 0.7698 - val_loss: 0.4957 - val_acc: 0.7567
Epoch 22/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4466 - acc: 0.7771

254/254 [==============================] - 1s 4ms/step - loss: 0.4466 - acc: 0.7771 - val_loss: 0.5453 - val_acc: 0.7389
Epoch 23/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4470 - acc: 0.7759

254/254 [==============================] - 1s 4ms/step - loss: 0.4470 - acc: 0.7759 - val_loss: 0.4973 - val_acc: 0.7638
Epoch 24/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4405 - acc: 0.7782

254/254 [==============================] - 1s 5ms/step - loss: 0.4405 - acc: 0.7782 - val_loss: 0.5049 - val_acc: 0.7620


Run completed: runs/2024-05-06T19-35-31Z

Training run 7/52 (flags = list(16, 64, 0.01, 20, 50, "tanh", "sigmoid", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-35-59Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:06 - loss: 0.7402 - acc: 0.6500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0297s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0297s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6552 - acc: 0.6219

254/254 [==============================] - 3s 6ms/step - loss: 0.6552 - acc: 0.6219 - val_loss: 0.5129 - val_acc: 0.7531
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5381 - acc: 0.7299

254/254 [==============================] - 1s 4ms/step - loss: 0.5381 - acc: 0.7299 - val_loss: 0.5031 - val_acc: 0.7638
Epoch 3/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5337 - acc: 0.7384

254/254 [==============================] - 1s 5ms/step - loss: 0.5337 - acc: 0.7384 - val_loss: 0.4952 - val_acc: 0.7638
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5233 - acc: 0.7426

254/254 [==============================] - 1s 4ms/step - loss: 0.5233 - acc: 0.7426 - val_loss: 0.4811 - val_acc: 0.7655
Epoch 5/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5153 - acc: 0.7447 - val_loss: 0.4799 - val_acc: 0.7620
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5141 - acc: 0.7457

254/254 [==============================] - 1s 5ms/step - loss: 0.5141 - acc: 0.7457 - val_loss: 0.5018 - val_acc: 0.7549
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5158 - acc: 0.7453

254/254 [==============================] - 1s 4ms/step - loss: 0.5158 - acc: 0.7453 - val_loss: 0.4697 - val_acc: 0.7762
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5082 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.5082 - acc: 0.7493 - val_loss: 0.4853 - val_acc: 0.7549
Epoch 9/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5038 - acc: 0.7512 - val_loss: 0.5048 - val_acc: 0.7549
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5064 - acc: 0.7487

254/254 [==============================] - 1s 4ms/step - loss: 0.5064 - acc: 0.7487 - val_loss: 0.4729 - val_acc: 0.7762
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5046 - acc: 0.7487

254/254 [==============================] - 1s 4ms/step - loss: 0.5046 - acc: 0.7487 - val_loss: 0.4789 - val_acc: 0.7584
Epoch 12/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5108 - acc: 0.7473 - val_loss: 0.4910 - val_acc: 0.7531
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5055 - acc: 0.7418

254/254 [==============================] - 1s 4ms/step - loss: 0.5055 - acc: 0.7418 - val_loss: 0.4852 - val_acc: 0.7567
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5010 - acc: 0.7475

254/254 [==============================] - 1s 4ms/step - loss: 0.5010 - acc: 0.7475 - val_loss: 0.4877 - val_acc: 0.7531
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5012 - acc: 0.7473

254/254 [==============================] - 1s 4ms/step - loss: 0.5012 - acc: 0.7473 - val_loss: 0.4956 - val_acc: 0.7513
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5026 - acc: 0.7461

254/254 [==============================] - 1s 4ms/step - loss: 0.5026 - acc: 0.7461 - val_loss: 0.4833 - val_acc: 0.7549
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4935 - acc: 0.7495

254/254 [==============================] - 1s 5ms/step - loss: 0.4935 - acc: 0.7495 - val_loss: 0.4794 - val_acc: 0.7567


Run completed: runs/2024-05-06T19-35-59Z

Training run 8/52 (flags = list(64, 64, 0.001, 20, 50, "relu", "sigmoid", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-36-20Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 6:04 - loss: 0.6301 - acc: 0.4000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


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254/254 [==============================] - 2s 4ms/step - loss: 0.5389 - acc: 0.7175

254/254 [==============================] - 3s 7ms/step - loss: 0.5389 - acc: 0.7175 - val_loss: 0.4698 - val_acc: 0.7709
Epoch 2/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5012 - acc: 0.7518

254/254 [==============================] - 1s 5ms/step - loss: 0.5012 - acc: 0.7518 - val_loss: 0.4669 - val_acc: 0.7691
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4947 - acc: 0.7499

254/254 [==============================] - 1s 5ms/step - loss: 0.4947 - acc: 0.7499 - val_loss: 0.4927 - val_acc: 0.7567
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4843 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4843 - acc: 0.7550 - val_loss: 0.4506 - val_acc: 0.7798
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4896 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.4896 - acc: 0.7514 - val_loss: 0.4950 - val_acc: 0.7602
Epoch 6/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4869 - acc: 0.7510

254/254 [==============================] - 1s 5ms/step - loss: 0.4869 - acc: 0.7510 - val_loss: 0.4610 - val_acc: 0.7744
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4853 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4853 - acc: 0.7556 - val_loss: 0.4739 - val_acc: 0.7744
Epoch 8/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4776 - acc: 0.7585 - val_loss: 0.4668 - val_acc: 0.7709
Epoch 9/50

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251/254 [============================>.] - ETA: 0s - loss: 0.4787 - acc: 0.7600
254/254 [==============================] - 1s 4ms/step - loss: 0.4776 - acc: 0.7605

254/254 [==============================] - 1s 5ms/step - loss: 0.4776 - acc: 0.7605 - val_loss: 0.4876 - val_acc: 0.7460
Epoch 10/50

  1/254 [..............................] - ETA: 0s - loss: 0.3862 - acc: 0.9000
 16/254 [>.............................] - ETA: 0s - loss: 0.5084 - acc: 0.7344
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 70/254 [=======>......................] - ETA: 0s - loss: 0.5119 - acc: 0.7457
 88/254 [=========>....................] - ETA: 0s - loss: 0.5043 - acc: 0.7403
105/254 [===========>..................] - ETA: 0s - loss: 0.4993 - acc: 0.7486
123/254 [=============>................] - ETA: 0s - loss: 0.4901 - acc: 0.7541
141/254 [===============>..............] - ETA: 0s - loss: 0.4870 - acc: 0.7557
159/254 [=================>............] - ETA: 0s - loss: 0.4805 - acc: 0.7588
177/254 [===================>..........] - ETA: 0s - loss: 0.4780 - acc: 0.7593
196/254 [======================>.......] - ETA: 0s - loss: 0.4781 - acc: 0.7625
204/254 [=======================>......] - ETA: 0s - loss: 0.4759 - acc: 0.7625
222/254 [=========================>....] - ETA: 0s - loss: 0.4766 - acc: 0.7619
240/254 [===========================>..] - ETA: 0s - loss: 0.4758 - acc: 0.7613
254/254 [==============================] - 1s 3ms/step - loss: 0.4741 - acc: 0.7621

254/254 [==============================] - 1s 4ms/step - loss: 0.4741 - acc: 0.7621 - val_loss: 0.4818 - val_acc: 0.7584
Epoch 11/50

  1/254 [..............................] - ETA: 0s - loss: 0.4283 - acc: 0.8000
 18/254 [=>............................] - ETA: 0s - loss: 0.5188 - acc: 0.7417
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 89/254 [=========>....................] - ETA: 0s - loss: 0.4793 - acc: 0.7500
107/254 [===========>..................] - ETA: 0s - loss: 0.4756 - acc: 0.7509
125/254 [=============>................] - ETA: 0s - loss: 0.4713 - acc: 0.7544
142/254 [===============>..............] - ETA: 0s - loss: 0.4713 - acc: 0.7560
161/254 [==================>...........] - ETA: 0s - loss: 0.4800 - acc: 0.7500
180/254 [====================>.........] - ETA: 0s - loss: 0.4764 - acc: 0.7542
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208/254 [=======================>......] - ETA: 0s - loss: 0.4703 - acc: 0.7577
226/254 [=========================>....] - ETA: 0s - loss: 0.4711 - acc: 0.7608
242/254 [===========================>..] - ETA: 0s - loss: 0.4730 - acc: 0.7583
254/254 [==============================] - 1s 3ms/step - loss: 0.4764 - acc: 0.7564

254/254 [==============================] - 1s 4ms/step - loss: 0.4764 - acc: 0.7564 - val_loss: 0.4770 - val_acc: 0.7709
Epoch 12/50

  1/254 [..............................] - ETA: 0s - loss: 0.6528 - acc: 0.6500
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114/254 [============>.................] - ETA: 0s - loss: 0.4692 - acc: 0.7544
131/254 [==============>...............] - ETA: 0s - loss: 0.4711 - acc: 0.7561
149/254 [================>.............] - ETA: 0s - loss: 0.4808 - acc: 0.7527
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185/254 [====================>.........] - ETA: 0s - loss: 0.4750 - acc: 0.7551
204/254 [=======================>......] - ETA: 0s - loss: 0.4691 - acc: 0.7574
222/254 [=========================>....] - ETA: 0s - loss: 0.4730 - acc: 0.7583
239/254 [===========================>..] - ETA: 0s - loss: 0.4751 - acc: 0.7579
254/254 [==============================] - 1s 4ms/step - loss: 0.4734 - acc: 0.7579

254/254 [==============================] - 1s 5ms/step - loss: 0.4734 - acc: 0.7579 - val_loss: 0.4994 - val_acc: 0.7425
Epoch 13/50

  1/254 [..............................] - ETA: 0s - loss: 0.5204 - acc: 0.6500
 18/254 [=>............................] - ETA: 0s - loss: 0.4494 - acc: 0.7444
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109/254 [===========>..................] - ETA: 0s - loss: 0.4689 - acc: 0.7619
126/254 [=============>................] - ETA: 0s - loss: 0.4731 - acc: 0.7591
144/254 [================>.............] - ETA: 0s - loss: 0.4769 - acc: 0.7590
149/254 [================>.............] - ETA: 0s - loss: 0.4740 - acc: 0.7601
165/254 [==================>...........] - ETA: 0s - loss: 0.4737 - acc: 0.7582
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222/254 [=========================>....] - ETA: 0s - loss: 0.4771 - acc: 0.7568
238/254 [===========================>..] - ETA: 0s - loss: 0.4732 - acc: 0.7605
254/254 [==============================] - 1s 3ms/step - loss: 0.4736 - acc: 0.7579

254/254 [==============================] - 1s 4ms/step - loss: 0.4736 - acc: 0.7579 - val_loss: 0.4751 - val_acc: 0.7762
Epoch 14/50

  1/254 [..............................] - ETA: 0s - loss: 0.3496 - acc: 0.8000
 18/254 [=>............................] - ETA: 0s - loss: 0.4035 - acc: 0.8000
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108/254 [===========>..................] - ETA: 0s - loss: 0.4570 - acc: 0.7731
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133/254 [==============>...............] - ETA: 0s - loss: 0.4569 - acc: 0.7722
147/254 [================>.............] - ETA: 0s - loss: 0.4652 - acc: 0.7670
161/254 [==================>...........] - ETA: 0s - loss: 0.4688 - acc: 0.7643
179/254 [====================>.........] - ETA: 0s - loss: 0.4636 - acc: 0.7709
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225/254 [=========================>....] - ETA: 0s - loss: 0.4674 - acc: 0.7689
243/254 [===========================>..] - ETA: 0s - loss: 0.4673 - acc: 0.7663
254/254 [==============================] - 1s 3ms/step - loss: 0.4699 - acc: 0.7660

254/254 [==============================] - 1s 4ms/step - loss: 0.4699 - acc: 0.7660 - val_loss: 0.4704 - val_acc: 0.7655


Run completed: runs/2024-05-06T19-36-20Z

Training run 9/52 (flags = list(32, 32, 0.01, 50, 50, "sigmoid", "tanh", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-36-38Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:01 - loss: 0.6867 - acc: 0.7000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.7265 - acc: 0.5863  
 34/102 [=========>....................] - ETA: 0s - loss: 0.6990 - acc: 0.5782
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 67/102 [==================>...........] - ETA: 0s - loss: 0.6670 - acc: 0.6113
 85/102 [========================>.....] - ETA: 0s - loss: 0.6569 - acc: 0.6207
102/102 [==============================] - 2s 3ms/step - loss: 0.6487 - acc: 0.6276

102/102 [==============================] - 2s 11ms/step - loss: 0.6487 - acc: 0.6276 - val_loss: 0.5247 - val_acc: 0.7584
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.6238 - acc: 0.6400
 14/102 [===>..........................] - ETA: 0s - loss: 0.6077 - acc: 0.6757
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 86/102 [========================>.....] - ETA: 0s - loss: 0.5654 - acc: 0.7051
102/102 [==============================] - 0s 4ms/step - loss: 0.5654 - acc: 0.7043

102/102 [==============================] - 1s 6ms/step - loss: 0.5654 - acc: 0.7043 - val_loss: 0.5260 - val_acc: 0.7282
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6400 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5281 - acc: 0.7133
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 94/102 [==========================>...] - ETA: 0s - loss: 0.5403 - acc: 0.7200
102/102 [==============================] - 0s 3ms/step - loss: 0.5399 - acc: 0.7215

102/102 [==============================] - 1s 6ms/step - loss: 0.5399 - acc: 0.7215 - val_loss: 0.5048 - val_acc: 0.7531
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.5493 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5595 - acc: 0.7153
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 77/102 [=====================>........] - ETA: 0s - loss: 0.5307 - acc: 0.7301
 96/102 [===========================>..] - ETA: 0s - loss: 0.5313 - acc: 0.7294
102/102 [==============================] - 0s 3ms/step - loss: 0.5302 - acc: 0.7299

102/102 [==============================] - 1s 6ms/step - loss: 0.5302 - acc: 0.7299 - val_loss: 0.4807 - val_acc: 0.7673
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.4453 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5147 - acc: 0.7422
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102/102 [==============================] - 0s 3ms/step - loss: 0.5210 - acc: 0.7374

102/102 [==============================] - 1s 5ms/step - loss: 0.5210 - acc: 0.7374 - val_loss: 0.4827 - val_acc: 0.7638
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.5310 - acc: 0.7800
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101/102 [============================>.] - ETA: 0s - loss: 0.5191 - acc: 0.7416
102/102 [==============================] - 0s 4ms/step - loss: 0.5183 - acc: 0.7422

102/102 [==============================] - 1s 7ms/step - loss: 0.5183 - acc: 0.7422 - val_loss: 0.4717 - val_acc: 0.7673
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.4168 - acc: 0.7600
 14/102 [===>..........................] - ETA: 0s - loss: 0.4728 - acc: 0.7500
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 67/102 [==================>...........] - ETA: 0s - loss: 0.5171 - acc: 0.7388
 86/102 [========================>.....] - ETA: 0s - loss: 0.5213 - acc: 0.7379
102/102 [==============================] - 0s 4ms/step - loss: 0.5198 - acc: 0.7394

102/102 [==============================] - 1s 7ms/step - loss: 0.5198 - acc: 0.7394 - val_loss: 0.4713 - val_acc: 0.7691
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.4674 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5238 - acc: 0.7611
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102/102 [==============================] - 0s 3ms/step - loss: 0.5133 - acc: 0.7376

102/102 [==============================] - 1s 5ms/step - loss: 0.5133 - acc: 0.7376 - val_loss: 0.4917 - val_acc: 0.7620
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.6855 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5501 - acc: 0.7165
 33/102 [========>.....................] - ETA: 0s - loss: 0.5265 - acc: 0.7273
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102/102 [==============================] - 0s 4ms/step - loss: 0.5097 - acc: 0.7337

102/102 [==============================] - 1s 6ms/step - loss: 0.5097 - acc: 0.7337 - val_loss: 0.4681 - val_acc: 0.7691
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.6165 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5561 - acc: 0.7244
 36/102 [=========>....................] - ETA: 0s - loss: 0.5256 - acc: 0.7367
 54/102 [==============>...............] - ETA: 0s - loss: 0.5200 - acc: 0.7393
 71/102 [===================>..........] - ETA: 0s - loss: 0.5223 - acc: 0.7363
 89/102 [=========================>....] - ETA: 0s - loss: 0.5198 - acc: 0.7384
102/102 [==============================] - 0s 3ms/step - loss: 0.5158 - acc: 0.7384

102/102 [==============================] - 1s 5ms/step - loss: 0.5158 - acc: 0.7384 - val_loss: 0.4798 - val_acc: 0.7638
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.6091 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5111 - acc: 0.7344
 35/102 [=========>....................] - ETA: 0s - loss: 0.5109 - acc: 0.7320
 52/102 [==============>...............] - ETA: 0s - loss: 0.5071 - acc: 0.7327
 69/102 [===================>..........] - ETA: 0s - loss: 0.5040 - acc: 0.7368
 88/102 [========================>.....] - ETA: 0s - loss: 0.5029 - acc: 0.7389
102/102 [==============================] - 0s 3ms/step - loss: 0.5069 - acc: 0.7368

102/102 [==============================] - 1s 6ms/step - loss: 0.5069 - acc: 0.7368 - val_loss: 0.4935 - val_acc: 0.7602
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.4264 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5063 - acc: 0.7482
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 86/102 [========================>.....] - ETA: 0s - loss: 0.5047 - acc: 0.7344
100/102 [============================>.] - ETA: 0s - loss: 0.5032 - acc: 0.7344
102/102 [==============================] - 0s 4ms/step - loss: 0.5020 - acc: 0.7353

102/102 [==============================] - 1s 7ms/step - loss: 0.5020 - acc: 0.7353 - val_loss: 0.4949 - val_acc: 0.7584
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.4996 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4750 - acc: 0.7656
 33/102 [========>.....................] - ETA: 0s - loss: 0.5039 - acc: 0.7503
 38/102 [==========>...................] - ETA: 0s - loss: 0.5098 - acc: 0.7458
 50/102 [=============>................] - ETA: 0s - loss: 0.5113 - acc: 0.7452
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 80/102 [======================>.......] - ETA: 0s - loss: 0.4988 - acc: 0.7412
 98/102 [===========================>..] - ETA: 0s - loss: 0.5024 - acc: 0.7386
102/102 [==============================] - 0s 4ms/step - loss: 0.5040 - acc: 0.7388

102/102 [==============================] - 1s 7ms/step - loss: 0.5040 - acc: 0.7388 - val_loss: 0.4856 - val_acc: 0.7620
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.6208 - acc: 0.6200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5031 - acc: 0.7287
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102/102 [==============================] - 0s 3ms/step - loss: 0.4987 - acc: 0.7424

102/102 [==============================] - 1s 6ms/step - loss: 0.4987 - acc: 0.7424 - val_loss: 0.5040 - val_acc: 0.7496
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.3586 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5133 - acc: 0.7412
 36/102 [=========>....................] - ETA: 0s - loss: 0.5047 - acc: 0.7478
 55/102 [===============>..............] - ETA: 0s - loss: 0.4984 - acc: 0.7440
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102/102 [==============================] - 0s 3ms/step - loss: 0.5018 - acc: 0.7489

102/102 [==============================] - 1s 6ms/step - loss: 0.5018 - acc: 0.7489 - val_loss: 0.4942 - val_acc: 0.7513
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.6258 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5241 - acc: 0.7176
 36/102 [=========>....................] - ETA: 0s - loss: 0.5237 - acc: 0.7272
 53/102 [==============>...............] - ETA: 0s - loss: 0.5138 - acc: 0.7298
 69/102 [===================>..........] - ETA: 0s - loss: 0.5093 - acc: 0.7345
 86/102 [========================>.....] - ETA: 0s - loss: 0.5036 - acc: 0.7391
102/102 [==============================] - 0s 3ms/step - loss: 0.5016 - acc: 0.7388

102/102 [==============================] - 1s 5ms/step - loss: 0.5016 - acc: 0.7388 - val_loss: 0.4813 - val_acc: 0.7655
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.5495 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5367 - acc: 0.7435
 25/102 [======>.......................] - ETA: 0s - loss: 0.5307 - acc: 0.7416
 40/102 [==========>...................] - ETA: 0s - loss: 0.5149 - acc: 0.7360
 53/102 [==============>...............] - ETA: 0s - loss: 0.5094 - acc: 0.7351
 68/102 [===================>..........] - ETA: 0s - loss: 0.5023 - acc: 0.7412
 84/102 [=======================>......] - ETA: 0s - loss: 0.5069 - acc: 0.7388
102/102 [==============================] - 0s 4ms/step - loss: 0.5011 - acc: 0.7443

102/102 [==============================] - 1s 6ms/step - loss: 0.5011 - acc: 0.7443 - val_loss: 0.4812 - val_acc: 0.7655
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.6207 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5152 - acc: 0.7400
 37/102 [=========>....................] - ETA: 0s - loss: 0.5129 - acc: 0.7405
 55/102 [===============>..............] - ETA: 0s - loss: 0.5064 - acc: 0.7433
 74/102 [====================>.........] - ETA: 0s - loss: 0.5049 - acc: 0.7427
 91/102 [=========================>....] - ETA: 0s - loss: 0.5001 - acc: 0.7457
102/102 [==============================] - 0s 3ms/step - loss: 0.4984 - acc: 0.7467

102/102 [==============================] - 1s 5ms/step - loss: 0.4984 - acc: 0.7467 - val_loss: 0.4769 - val_acc: 0.7691
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.4741 - acc: 0.8200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5372 - acc: 0.7437
 34/102 [=========>....................] - ETA: 0s - loss: 0.5158 - acc: 0.7441
 51/102 [==============>...............] - ETA: 0s - loss: 0.5105 - acc: 0.7455
 68/102 [===================>..........] - ETA: 0s - loss: 0.5081 - acc: 0.7435
 70/102 [===================>..........] - ETA: 0s - loss: 0.5083 - acc: 0.7417
 88/102 [========================>.....] - ETA: 0s - loss: 0.5016 - acc: 0.7443
102/102 [==============================] - 0s 4ms/step - loss: 0.5032 - acc: 0.7426

102/102 [==============================] - 1s 7ms/step - loss: 0.5032 - acc: 0.7426 - val_loss: 0.4806 - val_acc: 0.7655


Run completed: runs/2024-05-06T19-36-38Z

Training run 10/52 (flags = list(64, 16, 0.01, 50, 30, "tanh", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-36-52Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:02 - loss: 0.4877 - acc: 0.7200

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 18/102 [====>.........................] - ETA: 0s - loss: 0.6114 - acc: 0.6867  
 36/102 [=========>....................] - ETA: 0s - loss: 0.5802 - acc: 0.6917
 53/102 [==============>...............] - ETA: 0s - loss: 0.5530 - acc: 0.7106
 70/102 [===================>..........] - ETA: 0s - loss: 0.5362 - acc: 0.7211
 88/102 [========================>.....] - ETA: 0s - loss: 0.5376 - acc: 0.7245
102/102 [==============================] - 2s 3ms/step - loss: 0.5357 - acc: 0.7276

102/102 [==============================] - 2s 11ms/step - loss: 0.5357 - acc: 0.7276 - val_loss: 0.5317 - val_acc: 0.7425
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.4946 - acc: 0.7000
 11/102 [==>...........................] - ETA: 0s - loss: 0.4894 - acc: 0.7436
 23/102 [=====>........................] - ETA: 0s - loss: 0.4972 - acc: 0.7522
 37/102 [=========>....................] - ETA: 0s - loss: 0.5182 - acc: 0.7514
 46/102 [============>.................] - ETA: 0s - loss: 0.5185 - acc: 0.7487
 54/102 [==============>...............] - ETA: 0s - loss: 0.5135 - acc: 0.7500
 66/102 [==================>...........] - ETA: 0s - loss: 0.5084 - acc: 0.7473
 80/102 [======================>.......] - ETA: 0s - loss: 0.5041 - acc: 0.7563
 90/102 [=========================>....] - ETA: 0s - loss: 0.5044 - acc: 0.7551
102/102 [==============================] - 0s 4ms/step - loss: 0.5048 - acc: 0.7540

102/102 [==============================] - 1s 7ms/step - loss: 0.5048 - acc: 0.7540 - val_loss: 0.5037 - val_acc: 0.7531
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.8034 - acc: 0.6400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5152 - acc: 0.7553
 35/102 [=========>....................] - ETA: 0s - loss: 0.4932 - acc: 0.7594
 53/102 [==============>...............] - ETA: 0s - loss: 0.4924 - acc: 0.7653
 70/102 [===================>..........] - ETA: 0s - loss: 0.4983 - acc: 0.7554
 86/102 [========================>.....] - ETA: 0s - loss: 0.5066 - acc: 0.7530
102/102 [==============================] - 0s 3ms/step - loss: 0.5030 - acc: 0.7546

102/102 [==============================] - 1s 6ms/step - loss: 0.5030 - acc: 0.7546 - val_loss: 0.4973 - val_acc: 0.7584
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.5813 - acc: 0.7400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5127 - acc: 0.7262
 25/102 [======>.......................] - ETA: 0s - loss: 0.5034 - acc: 0.7336
 42/102 [===========>..................] - ETA: 0s - loss: 0.4981 - acc: 0.7495
 58/102 [================>.............] - ETA: 0s - loss: 0.4829 - acc: 0.7628
 70/102 [===================>..........] - ETA: 0s - loss: 0.4915 - acc: 0.7606
 87/102 [========================>.....] - ETA: 0s - loss: 0.4921 - acc: 0.7572
102/102 [==============================] - 0s 4ms/step - loss: 0.4923 - acc: 0.7571

102/102 [==============================] - 1s 6ms/step - loss: 0.4923 - acc: 0.7571 - val_loss: 0.4943 - val_acc: 0.7567
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.4640 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5305 - acc: 0.7188
 35/102 [=========>....................] - ETA: 0s - loss: 0.5018 - acc: 0.7400
 53/102 [==============>...............] - ETA: 0s - loss: 0.4935 - acc: 0.7577
 71/102 [===================>..........] - ETA: 0s - loss: 0.4841 - acc: 0.7569
 81/102 [======================>.......] - ETA: 0s - loss: 0.4842 - acc: 0.7578
 89/102 [=========================>....] - ETA: 0s - loss: 0.4952 - acc: 0.7548
102/102 [==============================] - 0s 4ms/step - loss: 0.4957 - acc: 0.7566

102/102 [==============================] - 1s 7ms/step - loss: 0.4957 - acc: 0.7566 - val_loss: 0.4978 - val_acc: 0.7496
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.4929 - acc: 0.8800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5096 - acc: 0.7337
 34/102 [=========>....................] - ETA: 0s - loss: 0.4885 - acc: 0.7512
 52/102 [==============>...............] - ETA: 0s - loss: 0.4894 - acc: 0.7485
 69/102 [===================>..........] - ETA: 0s - loss: 0.4992 - acc: 0.7496
 83/102 [=======================>......] - ETA: 0s - loss: 0.5000 - acc: 0.7492
100/102 [============================>.] - ETA: 0s - loss: 0.4946 - acc: 0.7530
102/102 [==============================] - 0s 3ms/step - loss: 0.4951 - acc: 0.7520

102/102 [==============================] - 1s 5ms/step - loss: 0.4951 - acc: 0.7520 - val_loss: 0.5044 - val_acc: 0.7513
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.6013 - acc: 0.6400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5274 - acc: 0.7378
 35/102 [=========>....................] - ETA: 0s - loss: 0.4977 - acc: 0.7451
 46/102 [============>.................] - ETA: 0s - loss: 0.4956 - acc: 0.7465
 61/102 [================>.............] - ETA: 0s - loss: 0.4981 - acc: 0.7495
 70/102 [===================>..........] - ETA: 0s - loss: 0.4903 - acc: 0.7546
 88/102 [========================>.....] - ETA: 0s - loss: 0.4871 - acc: 0.7545
102/102 [==============================] - 0s 4ms/step - loss: 0.4904 - acc: 0.7562

102/102 [==============================] - 1s 6ms/step - loss: 0.4904 - acc: 0.7562 - val_loss: 0.4770 - val_acc: 0.7691
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.4558 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5176 - acc: 0.7388
 35/102 [=========>....................] - ETA: 0s - loss: 0.4970 - acc: 0.7469
 51/102 [==============>...............] - ETA: 0s - loss: 0.5069 - acc: 0.7463
 69/102 [===================>..........] - ETA: 0s - loss: 0.4965 - acc: 0.7493
 86/102 [========================>.....] - ETA: 0s - loss: 0.4923 - acc: 0.7498
102/102 [==============================] - 0s 3ms/step - loss: 0.4892 - acc: 0.7522

102/102 [==============================] - 1s 6ms/step - loss: 0.4892 - acc: 0.7522 - val_loss: 0.4829 - val_acc: 0.7620
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4615 - acc: 0.7000
 12/102 [==>...........................] - ETA: 0s - loss: 0.4778 - acc: 0.7517
 30/102 [=======>......................] - ETA: 0s - loss: 0.4798 - acc: 0.7600
 37/102 [=========>....................] - ETA: 0s - loss: 0.4788 - acc: 0.7578
 53/102 [==============>...............] - ETA: 0s - loss: 0.4824 - acc: 0.7585
 70/102 [===================>..........] - ETA: 0s - loss: 0.4877 - acc: 0.7560
 87/102 [========================>.....] - ETA: 0s - loss: 0.4883 - acc: 0.7563
100/102 [============================>.] - ETA: 0s - loss: 0.4861 - acc: 0.7560
102/102 [==============================] - 0s 4ms/step - loss: 0.4855 - acc: 0.7568

102/102 [==============================] - 1s 6ms/step - loss: 0.4855 - acc: 0.7568 - val_loss: 0.4767 - val_acc: 0.7726
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.6117 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5106 - acc: 0.7553
 35/102 [=========>....................] - ETA: 0s - loss: 0.5010 - acc: 0.7543
 53/102 [==============>...............] - ETA: 0s - loss: 0.4875 - acc: 0.7626
 71/102 [===================>..........] - ETA: 0s - loss: 0.4914 - acc: 0.7569
 88/102 [========================>.....] - ETA: 0s - loss: 0.4869 - acc: 0.7591
 99/102 [============================>.] - ETA: 0s - loss: 0.4920 - acc: 0.7537
102/102 [==============================] - 0s 3ms/step - loss: 0.4900 - acc: 0.7548

102/102 [==============================] - 1s 6ms/step - loss: 0.4900 - acc: 0.7548 - val_loss: 0.4867 - val_acc: 0.7709
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5273 - acc: 0.8200
 16/102 [===>..........................] - ETA: 0s - loss: 0.4635 - acc: 0.7763
 35/102 [=========>....................] - ETA: 0s - loss: 0.4657 - acc: 0.7789
 52/102 [==============>...............] - ETA: 0s - loss: 0.4650 - acc: 0.7800
 70/102 [===================>..........] - ETA: 0s - loss: 0.4891 - acc: 0.7611
 88/102 [========================>.....] - ETA: 0s - loss: 0.4909 - acc: 0.7600
102/102 [==============================] - 0s 3ms/step - loss: 0.4863 - acc: 0.7599

102/102 [==============================] - 1s 5ms/step - loss: 0.4863 - acc: 0.7599 - val_loss: 0.4979 - val_acc: 0.7442
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5910 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4880 - acc: 0.7576
 34/102 [=========>....................] - ETA: 0s - loss: 0.4875 - acc: 0.7600
 52/102 [==============>...............] - ETA: 0s - loss: 0.4882 - acc: 0.7608
 59/102 [================>.............] - ETA: 0s - loss: 0.4852 - acc: 0.7641
 77/102 [=====================>........] - ETA: 0s - loss: 0.4869 - acc: 0.7595
 89/102 [=========================>....] - ETA: 0s - loss: 0.4826 - acc: 0.7611
 98/102 [===========================>..] - ETA: 0s - loss: 0.4878 - acc: 0.7600
102/102 [==============================] - 0s 5ms/step - loss: 0.4902 - acc: 0.7583

102/102 [==============================] - 1s 7ms/step - loss: 0.4902 - acc: 0.7583 - val_loss: 0.4854 - val_acc: 0.7531
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.5212 - acc: 0.6600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4807 - acc: 0.7367
 35/102 [=========>....................] - ETA: 0s - loss: 0.4713 - acc: 0.7623
 52/102 [==============>...............] - ETA: 0s - loss: 0.4768 - acc: 0.7604
 70/102 [===================>..........] - ETA: 0s - loss: 0.4851 - acc: 0.7571
 88/102 [========================>.....] - ETA: 0s - loss: 0.4862 - acc: 0.7557
102/102 [==============================] - 0s 3ms/step - loss: 0.4839 - acc: 0.7568

102/102 [==============================] - 1s 5ms/step - loss: 0.4839 - acc: 0.7568 - val_loss: 0.4867 - val_acc: 0.7531
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.4031 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4820 - acc: 0.7529
 34/102 [=========>....................] - ETA: 0s - loss: 0.4654 - acc: 0.7676
 49/102 [=============>................] - ETA: 0s - loss: 0.4763 - acc: 0.7657
 66/102 [==================>...........] - ETA: 0s - loss: 0.4760 - acc: 0.7645
 83/102 [=======================>......] - ETA: 0s - loss: 0.4838 - acc: 0.7588
100/102 [============================>.] - ETA: 0s - loss: 0.4820 - acc: 0.7598
102/102 [==============================] - 0s 3ms/step - loss: 0.4812 - acc: 0.7601

102/102 [==============================] - 1s 5ms/step - loss: 0.4812 - acc: 0.7601 - val_loss: 0.4726 - val_acc: 0.7691
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5688 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4888 - acc: 0.7624
 36/102 [=========>....................] - ETA: 0s - loss: 0.4841 - acc: 0.7561
 54/102 [==============>...............] - ETA: 0s - loss: 0.4835 - acc: 0.7611
 70/102 [===================>..........] - ETA: 0s - loss: 0.4845 - acc: 0.7617
 87/102 [========================>.....] - ETA: 0s - loss: 0.4824 - acc: 0.7575
 99/102 [============================>.] - ETA: 0s - loss: 0.4833 - acc: 0.7590
102/102 [==============================] - 0s 3ms/step - loss: 0.4838 - acc: 0.7587

102/102 [==============================] - 1s 6ms/step - loss: 0.4838 - acc: 0.7587 - val_loss: 0.4574 - val_acc: 0.7798
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.5982 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4877 - acc: 0.7718
 35/102 [=========>....................] - ETA: 0s - loss: 0.4724 - acc: 0.7703
 52/102 [==============>...............] - ETA: 0s - loss: 0.4912 - acc: 0.7577
 69/102 [===================>..........] - ETA: 0s - loss: 0.4848 - acc: 0.7600
 88/102 [========================>.....] - ETA: 0s - loss: 0.4834 - acc: 0.7595
102/102 [==============================] - 0s 3ms/step - loss: 0.4856 - acc: 0.7583

102/102 [==============================] - 1s 5ms/step - loss: 0.4856 - acc: 0.7583 - val_loss: 0.5067 - val_acc: 0.7549
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.3896 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5381 - acc: 0.7071
 34/102 [=========>....................] - ETA: 0s - loss: 0.5010 - acc: 0.7441
 46/102 [============>.................] - ETA: 0s - loss: 0.4961 - acc: 0.7422
 63/102 [=================>............] - ETA: 0s - loss: 0.4943 - acc: 0.7470
 76/102 [=====================>........] - ETA: 0s - loss: 0.4913 - acc: 0.7532
 92/102 [==========================>...] - ETA: 0s - loss: 0.4852 - acc: 0.7524
102/102 [==============================] - 0s 4ms/step - loss: 0.4869 - acc: 0.7518

102/102 [==============================] - 1s 6ms/step - loss: 0.4869 - acc: 0.7518 - val_loss: 0.4782 - val_acc: 0.7673
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4043 - acc: 0.8800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5096 - acc: 0.7475
 34/102 [=========>....................] - ETA: 0s - loss: 0.4885 - acc: 0.7547
 42/102 [===========>..................] - ETA: 0s - loss: 0.4863 - acc: 0.7524
 53/102 [==============>...............] - ETA: 0s - loss: 0.4874 - acc: 0.7547
 63/102 [=================>............] - ETA: 0s - loss: 0.4832 - acc: 0.7600
 81/102 [======================>.......] - ETA: 0s - loss: 0.4759 - acc: 0.7607
 99/102 [============================>.] - ETA: 0s - loss: 0.4830 - acc: 0.7564
102/102 [==============================] - 0s 4ms/step - loss: 0.4823 - acc: 0.7560

102/102 [==============================] - 1s 7ms/step - loss: 0.4823 - acc: 0.7560 - val_loss: 0.4814 - val_acc: 0.7673
Epoch 19/30

  1/102 [..............................] - ETA: 0s - loss: 0.4055 - acc: 0.8600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4687 - acc: 0.7659
 31/102 [========>.....................] - ETA: 0s - loss: 0.4789 - acc: 0.7645
 48/102 [=============>................] - ETA: 0s - loss: 0.4852 - acc: 0.7521
 63/102 [=================>............] - ETA: 0s - loss: 0.4781 - acc: 0.7556
 80/102 [======================>.......] - ETA: 0s - loss: 0.4780 - acc: 0.7610
 98/102 [===========================>..] - ETA: 0s - loss: 0.4782 - acc: 0.7602
102/102 [==============================] - 0s 3ms/step - loss: 0.4771 - acc: 0.7613

102/102 [==============================] - 1s 5ms/step - loss: 0.4771 - acc: 0.7613 - val_loss: 0.4780 - val_acc: 0.7567
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.4458 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4657 - acc: 0.7835
 36/102 [=========>....................] - ETA: 0s - loss: 0.4907 - acc: 0.7600
 54/102 [==============>...............] - ETA: 0s - loss: 0.4849 - acc: 0.7685
 71/102 [===================>..........] - ETA: 0s - loss: 0.4873 - acc: 0.7606
 89/102 [=========================>....] - ETA: 0s - loss: 0.4817 - acc: 0.7661
102/102 [==============================] - 0s 3ms/step - loss: 0.4807 - acc: 0.7652

102/102 [==============================] - 1s 6ms/step - loss: 0.4807 - acc: 0.7652 - val_loss: 0.4802 - val_acc: 0.7691
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.6453 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4859 - acc: 0.7647
 34/102 [=========>....................] - ETA: 0s - loss: 0.4567 - acc: 0.7771
 51/102 [==============>...............] - ETA: 0s - loss: 0.4703 - acc: 0.7769
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 85/102 [========================>.....] - ETA: 0s - loss: 0.4738 - acc: 0.7685
102/102 [==============================] - 0s 3ms/step - loss: 0.4816 - acc: 0.7638

102/102 [==============================] - 1s 5ms/step - loss: 0.4816 - acc: 0.7638 - val_loss: 0.5080 - val_acc: 0.7513
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.3567 - acc: 0.8200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4551 - acc: 0.7556
 35/102 [=========>....................] - ETA: 0s - loss: 0.4798 - acc: 0.7543
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 63/102 [=================>............] - ETA: 0s - loss: 0.4818 - acc: 0.7565
 78/102 [=====================>........] - ETA: 0s - loss: 0.4790 - acc: 0.7590
 94/102 [==========================>...] - ETA: 0s - loss: 0.4820 - acc: 0.7557
102/102 [==============================] - 0s 4ms/step - loss: 0.4821 - acc: 0.7575

102/102 [==============================] - 1s 6ms/step - loss: 0.4821 - acc: 0.7575 - val_loss: 0.4760 - val_acc: 0.7602
Epoch 23/30

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102/102 [==============================] - 0s 3ms/step - loss: 0.4787 - acc: 0.7587

102/102 [==============================] - 1s 5ms/step - loss: 0.4787 - acc: 0.7587 - val_loss: 0.4851 - val_acc: 0.7584
Epoch 24/30

  1/102 [..............................] - ETA: 0s - loss: 0.4659 - acc: 0.8000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4814 - acc: 0.7564

102/102 [==============================] - 1s 7ms/step - loss: 0.4814 - acc: 0.7564 - val_loss: 0.4704 - val_acc: 0.7762
Epoch 25/30

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102/102 [==============================] - 0s 3ms/step - loss: 0.4807 - acc: 0.7621

102/102 [==============================] - 1s 6ms/step - loss: 0.4807 - acc: 0.7621 - val_loss: 0.4810 - val_acc: 0.7691


Run completed: runs/2024-05-06T19-36-52Z

Training run 11/52 (flags = list(32, 32, 0.01, 20, 30, "relu", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-37-14Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:05 - loss: 0.8126 - acc: 0.7000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6009 - acc: 0.6885

254/254 [==============================] - 3s 6ms/step - loss: 0.6009 - acc: 0.6885 - val_loss: 0.4995 - val_acc: 0.7300
Epoch 2/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5295 - acc: 0.7248

254/254 [==============================] - 1s 4ms/step - loss: 0.5295 - acc: 0.7248 - val_loss: 0.4773 - val_acc: 0.7549
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5148 - acc: 0.7376

254/254 [==============================] - 1s 5ms/step - loss: 0.5148 - acc: 0.7376 - val_loss: 0.5041 - val_acc: 0.7371
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5082 - acc: 0.7414

254/254 [==============================] - 1s 4ms/step - loss: 0.5082 - acc: 0.7414 - val_loss: 0.4875 - val_acc: 0.7602
Epoch 5/30

  1/254 [..............................] - ETA: 0s - loss: 0.6468 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4993 - acc: 0.7443

254/254 [==============================] - 1s 4ms/step - loss: 0.4993 - acc: 0.7443 - val_loss: 0.4826 - val_acc: 0.7691
Epoch 6/30

  1/254 [..............................] - ETA: 0s - loss: 0.4750 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4930 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4930 - acc: 0.7550 - val_loss: 0.4703 - val_acc: 0.7726
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5042 - acc: 0.7481

254/254 [==============================] - 1s 4ms/step - loss: 0.5042 - acc: 0.7481 - val_loss: 0.4892 - val_acc: 0.7602
Epoch 8/30

  1/254 [..............................] - ETA: 2s - loss: 0.6181 - acc: 0.4000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4978 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.4978 - acc: 0.7493 - val_loss: 0.4790 - val_acc: 0.7638
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4904 - acc: 0.7538

254/254 [==============================] - 1s 4ms/step - loss: 0.4904 - acc: 0.7538 - val_loss: 0.4769 - val_acc: 0.7655
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4937 - acc: 0.7534

254/254 [==============================] - 1s 4ms/step - loss: 0.4937 - acc: 0.7534 - val_loss: 0.4811 - val_acc: 0.7638
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4860 - acc: 0.7554

254/254 [==============================] - 1s 4ms/step - loss: 0.4860 - acc: 0.7554 - val_loss: 0.4999 - val_acc: 0.7460
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4841 - acc: 0.7554

254/254 [==============================] - 1s 4ms/step - loss: 0.4841 - acc: 0.7554 - val_loss: 0.5096 - val_acc: 0.7425
Epoch 13/30

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190/254 [=====================>........] - ETA: 0s - loss: 0.4831 - acc: 0.7653
207/254 [=======================>......] - ETA: 0s - loss: 0.4856 - acc: 0.7626
225/254 [=========================>....] - ETA: 0s - loss: 0.4886 - acc: 0.7618
245/254 [===========================>..] - ETA: 0s - loss: 0.4908 - acc: 0.7588
254/254 [==============================] - 1s 3ms/step - loss: 0.4897 - acc: 0.7585

254/254 [==============================] - 1s 4ms/step - loss: 0.4897 - acc: 0.7585 - val_loss: 0.4990 - val_acc: 0.7371
Epoch 14/30

  1/254 [..............................] - ETA: 0s - loss: 0.4894 - acc: 0.7000
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108/254 [===========>..................] - ETA: 0s - loss: 0.4857 - acc: 0.7569
127/254 [==============>...............] - ETA: 0s - loss: 0.4850 - acc: 0.7579
144/254 [================>.............] - ETA: 0s - loss: 0.4826 - acc: 0.7583
162/254 [==================>...........] - ETA: 0s - loss: 0.4815 - acc: 0.7552
180/254 [====================>.........] - ETA: 0s - loss: 0.4768 - acc: 0.7589
198/254 [======================>.......] - ETA: 0s - loss: 0.4787 - acc: 0.7571
216/254 [========================>.....] - ETA: 0s - loss: 0.4837 - acc: 0.7569
236/254 [==========================>...] - ETA: 0s - loss: 0.4861 - acc: 0.7538
254/254 [==============================] - 1s 3ms/step - loss: 0.4826 - acc: 0.7562

254/254 [==============================] - 1s 4ms/step - loss: 0.4826 - acc: 0.7562 - val_loss: 0.4885 - val_acc: 0.7478
Epoch 15/30

  1/254 [..............................] - ETA: 0s - loss: 0.3796 - acc: 0.8000
 17/254 [=>............................] - ETA: 0s - loss: 0.4724 - acc: 0.7647
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102/254 [===========>..................] - ETA: 0s - loss: 0.4684 - acc: 0.7598
122/254 [=============>................] - ETA: 0s - loss: 0.4626 - acc: 0.7623
139/254 [===============>..............] - ETA: 0s - loss: 0.4655 - acc: 0.7637
159/254 [=================>............] - ETA: 0s - loss: 0.4722 - acc: 0.7597
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211/254 [=======================>......] - ETA: 0s - loss: 0.4816 - acc: 0.7547
229/254 [==========================>...] - ETA: 0s - loss: 0.4797 - acc: 0.7533
249/254 [============================>.] - ETA: 0s - loss: 0.4776 - acc: 0.7550
254/254 [==============================] - 1s 3ms/step - loss: 0.4790 - acc: 0.7544

254/254 [==============================] - 1s 4ms/step - loss: 0.4790 - acc: 0.7544 - val_loss: 0.4805 - val_acc: 0.7673
Epoch 16/30

  1/254 [..............................] - ETA: 0s - loss: 0.4276 - acc: 0.7500
 18/254 [=>............................] - ETA: 0s - loss: 0.4896 - acc: 0.7306
 36/254 [===>..........................] - ETA: 0s - loss: 0.5093 - acc: 0.7361
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112/254 [============>.................] - ETA: 0s - loss: 0.4808 - acc: 0.7473
131/254 [==============>...............] - ETA: 0s - loss: 0.4716 - acc: 0.7531
149/254 [================>.............] - ETA: 0s - loss: 0.4718 - acc: 0.7564
168/254 [==================>...........] - ETA: 0s - loss: 0.4704 - acc: 0.7601
187/254 [=====================>........] - ETA: 0s - loss: 0.4762 - acc: 0.7596
206/254 [=======================>......] - ETA: 0s - loss: 0.4786 - acc: 0.7587
226/254 [=========================>....] - ETA: 0s - loss: 0.4819 - acc: 0.7584
245/254 [===========================>..] - ETA: 0s - loss: 0.4808 - acc: 0.7571
254/254 [==============================] - 1s 3ms/step - loss: 0.4793 - acc: 0.7573

254/254 [==============================] - 1s 4ms/step - loss: 0.4793 - acc: 0.7573 - val_loss: 0.4786 - val_acc: 0.7691


Run completed: runs/2024-05-06T19-37-14Z

Training run 12/52 (flags = list(64, 16, 0.001, 50, 30, "relu", "relu", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-37-33Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:30 - loss: 0.5569 - acc: 0.6600

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0024s vs `on_train_batch_end` time: 0.0783s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0024s vs `on_train_batch_end` time: 0.0783s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.6615 - acc: 0.6350  
 33/102 [========>.....................] - ETA: 0s - loss: 0.6774 - acc: 0.6236
 50/102 [=============>................] - ETA: 0s - loss: 0.6574 - acc: 0.6276
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 79/102 [======================>.......] - ETA: 0s - loss: 0.6506 - acc: 0.6314
 97/102 [===========================>..] - ETA: 0s - loss: 0.6412 - acc: 0.6384
102/102 [==============================] - 2s 3ms/step - loss: 0.6380 - acc: 0.6395

102/102 [==============================] - 3s 11ms/step - loss: 0.6380 - acc: 0.6395 - val_loss: 0.5383 - val_acc: 0.7140
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.6455 - acc: 0.6200
 13/102 [==>...........................] - ETA: 0s - loss: 0.6275 - acc: 0.6385
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 97/102 [===========================>..] - ETA: 0s - loss: 0.5733 - acc: 0.6759
102/102 [==============================] - 1s 5ms/step - loss: 0.5713 - acc: 0.6769

102/102 [==============================] - 1s 7ms/step - loss: 0.5713 - acc: 0.6769 - val_loss: 0.5154 - val_acc: 0.7265
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.4559 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5315 - acc: 0.6953
 30/102 [=======>......................] - ETA: 0s - loss: 0.5392 - acc: 0.7067
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 75/102 [=====================>........] - ETA: 0s - loss: 0.5392 - acc: 0.7091
 92/102 [==========================>...] - ETA: 0s - loss: 0.5463 - acc: 0.7037
102/102 [==============================] - 0s 3ms/step - loss: 0.5432 - acc: 0.7043

102/102 [==============================] - 1s 6ms/step - loss: 0.5432 - acc: 0.7043 - val_loss: 0.5005 - val_acc: 0.7353
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.6360 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5343 - acc: 0.7153
 35/102 [=========>....................] - ETA: 0s - loss: 0.5198 - acc: 0.7183
 51/102 [==============>...............] - ETA: 0s - loss: 0.5208 - acc: 0.7200
 55/102 [===============>..............] - ETA: 0s - loss: 0.5186 - acc: 0.7204
 68/102 [===================>..........] - ETA: 0s - loss: 0.5151 - acc: 0.7235
 85/102 [========================>.....] - ETA: 0s - loss: 0.5147 - acc: 0.7282
101/102 [============================>.] - ETA: 0s - loss: 0.5181 - acc: 0.7269
102/102 [==============================] - 0s 4ms/step - loss: 0.5179 - acc: 0.7274

102/102 [==============================] - 1s 7ms/step - loss: 0.5179 - acc: 0.7274 - val_loss: 0.4906 - val_acc: 0.7460
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.6030 - acc: 0.7000
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 85/102 [========================>.....] - ETA: 0s - loss: 0.5138 - acc: 0.7233
102/102 [==============================] - 0s 3ms/step - loss: 0.5200 - acc: 0.7250

102/102 [==============================] - 1s 5ms/step - loss: 0.5200 - acc: 0.7250 - val_loss: 0.5011 - val_acc: 0.7318
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.6501 - acc: 0.7000
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 98/102 [===========================>..] - ETA: 0s - loss: 0.5225 - acc: 0.7143
102/102 [==============================] - 0s 3ms/step - loss: 0.5210 - acc: 0.7159

102/102 [==============================] - 1s 6ms/step - loss: 0.5210 - acc: 0.7159 - val_loss: 0.4924 - val_acc: 0.7442
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5832 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5419 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.5125 - acc: 0.7290

102/102 [==============================] - 1s 7ms/step - loss: 0.5125 - acc: 0.7290 - val_loss: 0.4992 - val_acc: 0.7336
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5277 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5023 - acc: 0.7378
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 88/102 [========================>.....] - ETA: 0s - loss: 0.5151 - acc: 0.7336
102/102 [==============================] - 0s 4ms/step - loss: 0.5181 - acc: 0.7292

102/102 [==============================] - 1s 7ms/step - loss: 0.5181 - acc: 0.7292 - val_loss: 0.4968 - val_acc: 0.7442
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4898 - acc: 0.7800
 14/102 [===>..........................] - ETA: 0s - loss: 0.5378 - acc: 0.7114
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102/102 [==============================] - 0s 4ms/step - loss: 0.5114 - acc: 0.7264

102/102 [==============================] - 1s 6ms/step - loss: 0.5114 - acc: 0.7264 - val_loss: 0.4895 - val_acc: 0.7442
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.5907 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5015 - acc: 0.7422
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 53/102 [==============>...............] - ETA: 0s - loss: 0.5124 - acc: 0.7423
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 88/102 [========================>.....] - ETA: 0s - loss: 0.5127 - acc: 0.7391
102/102 [==============================] - 0s 3ms/step - loss: 0.5105 - acc: 0.7368

102/102 [==============================] - 1s 6ms/step - loss: 0.5105 - acc: 0.7368 - val_loss: 0.4830 - val_acc: 0.7567
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5721 - acc: 0.6600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5325 - acc: 0.7333
 36/102 [=========>....................] - ETA: 0s - loss: 0.5271 - acc: 0.7439
 54/102 [==============>...............] - ETA: 0s - loss: 0.5253 - acc: 0.7407
 71/102 [===================>..........] - ETA: 0s - loss: 0.5148 - acc: 0.7386
 89/102 [=========================>....] - ETA: 0s - loss: 0.5095 - acc: 0.7409
102/102 [==============================] - 0s 3ms/step - loss: 0.5102 - acc: 0.7400

102/102 [==============================] - 1s 5ms/step - loss: 0.5102 - acc: 0.7400 - val_loss: 0.4807 - val_acc: 0.7584
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5377 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5264 - acc: 0.7353
 36/102 [=========>....................] - ETA: 0s - loss: 0.5155 - acc: 0.7289
 53/102 [==============>...............] - ETA: 0s - loss: 0.5044 - acc: 0.7374
 60/102 [================>.............] - ETA: 0s - loss: 0.5063 - acc: 0.7390
 77/102 [=====================>........] - ETA: 0s - loss: 0.5021 - acc: 0.7345
 94/102 [==========================>...] - ETA: 0s - loss: 0.5057 - acc: 0.7340
102/102 [==============================] - 0s 3ms/step - loss: 0.5042 - acc: 0.7366

102/102 [==============================] - 1s 6ms/step - loss: 0.5042 - acc: 0.7366 - val_loss: 0.4879 - val_acc: 0.7496
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.4667 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5019 - acc: 0.7356
 36/102 [=========>....................] - ETA: 0s - loss: 0.5086 - acc: 0.7389
 54/102 [==============>...............] - ETA: 0s - loss: 0.5127 - acc: 0.7367
 71/102 [===================>..........] - ETA: 0s - loss: 0.5098 - acc: 0.7389
 88/102 [========================>.....] - ETA: 0s - loss: 0.5040 - acc: 0.7370
102/102 [==============================] - 0s 3ms/step - loss: 0.5087 - acc: 0.7378

102/102 [==============================] - 1s 6ms/step - loss: 0.5087 - acc: 0.7378 - val_loss: 0.4792 - val_acc: 0.7620
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.3927 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5367 - acc: 0.7163
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102/102 [==============================] - 0s 3ms/step - loss: 0.4960 - acc: 0.7437

102/102 [==============================] - 1s 6ms/step - loss: 0.4960 - acc: 0.7437 - val_loss: 0.4887 - val_acc: 0.7496
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5241 - acc: 0.6600
 13/102 [==>...........................] - ETA: 0s - loss: 0.5018 - acc: 0.7508
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101/102 [============================>.] - ETA: 0s - loss: 0.5000 - acc: 0.7491
102/102 [==============================] - 0s 3ms/step - loss: 0.4990 - acc: 0.7495

102/102 [==============================] - 1s 6ms/step - loss: 0.4990 - acc: 0.7495 - val_loss: 0.4872 - val_acc: 0.7567
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.2809 - acc: 0.8400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4990 - acc: 0.7741
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 70/102 [===================>..........] - ETA: 0s - loss: 0.4979 - acc: 0.7586
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102/102 [==============================] - 0s 3ms/step - loss: 0.5013 - acc: 0.7587

102/102 [==============================] - 1s 5ms/step - loss: 0.5013 - acc: 0.7587 - val_loss: 0.4897 - val_acc: 0.7549
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.4929 - acc: 0.8200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5280 - acc: 0.7200
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 89/102 [=========================>....] - ETA: 0s - loss: 0.5101 - acc: 0.7409
102/102 [==============================] - 0s 3ms/step - loss: 0.5026 - acc: 0.7441

102/102 [==============================] - 1s 5ms/step - loss: 0.5026 - acc: 0.7441 - val_loss: 0.4801 - val_acc: 0.7638
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4422 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5590 - acc: 0.7494
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102/102 [==============================] - 0s 3ms/step - loss: 0.5071 - acc: 0.7518

102/102 [==============================] - 1s 6ms/step - loss: 0.5071 - acc: 0.7518 - val_loss: 0.4835 - val_acc: 0.7655
Epoch 19/30

  1/102 [..............................] - ETA: 0s - loss: 0.7095 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5343 - acc: 0.7322
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 88/102 [========================>.....] - ETA: 0s - loss: 0.4987 - acc: 0.7534
102/102 [==============================] - 0s 4ms/step - loss: 0.5017 - acc: 0.7538

102/102 [==============================] - 1s 6ms/step - loss: 0.5017 - acc: 0.7538 - val_loss: 0.4869 - val_acc: 0.7496
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.3926 - acc: 0.7800
 19/102 [====>.........................] - ETA: 0s - loss: 0.4864 - acc: 0.7463
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 99/102 [============================>.] - ETA: 0s - loss: 0.4964 - acc: 0.7463
102/102 [==============================] - 0s 4ms/step - loss: 0.4955 - acc: 0.7455

102/102 [==============================] - 1s 10ms/step - loss: 0.4955 - acc: 0.7455 - val_loss: 0.4921 - val_acc: 0.7513
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.5217 - acc: 0.7200
 20/102 [====>.........................] - ETA: 0s - loss: 0.4916 - acc: 0.7480
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 52/102 [==============>...............] - ETA: 0s - loss: 0.5076 - acc: 0.7323
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 80/102 [======================>.......] - ETA: 0s - loss: 0.5048 - acc: 0.7350
 98/102 [===========================>..] - ETA: 0s - loss: 0.4947 - acc: 0.7392
102/102 [==============================] - 0s 4ms/step - loss: 0.4948 - acc: 0.7416

102/102 [==============================] - 1s 10ms/step - loss: 0.4948 - acc: 0.7416 - val_loss: 0.4723 - val_acc: 0.7655
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.4193 - acc: 0.8000
 12/102 [==>...........................] - ETA: 0s - loss: 0.4794 - acc: 0.7850
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102/102 [==============================] - 0s 3ms/step - loss: 0.4883 - acc: 0.7585

102/102 [==============================] - 1s 6ms/step - loss: 0.4883 - acc: 0.7585 - val_loss: 0.4911 - val_acc: 0.7620
Epoch 23/30

  1/102 [..............................] - ETA: 0s - loss: 0.5173 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4932 - acc: 0.7506
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102/102 [==============================] - 0s 3ms/step - loss: 0.4943 - acc: 0.7518

102/102 [==============================] - 1s 6ms/step - loss: 0.4943 - acc: 0.7518 - val_loss: 0.4791 - val_acc: 0.7691
Epoch 24/30

  1/102 [..............................] - ETA: 0s - loss: 0.4853 - acc: 0.7200
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102/102 [==============================] - 0s 4ms/step - loss: 0.4909 - acc: 0.7465

102/102 [==============================] - 1s 6ms/step - loss: 0.4909 - acc: 0.7465 - val_loss: 0.4930 - val_acc: 0.7620
Epoch 25/30

  1/102 [..............................] - ETA: 0s - loss: 0.6586 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.4859 - acc: 0.7530

102/102 [==============================] - 1s 5ms/step - loss: 0.4859 - acc: 0.7530 - val_loss: 0.4802 - val_acc: 0.7602
Epoch 26/30

  1/102 [..............................] - ETA: 0s - loss: 0.3395 - acc: 0.7800
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102/102 [==============================] - 0s 4ms/step - loss: 0.4905 - acc: 0.7562

102/102 [==============================] - 1s 7ms/step - loss: 0.4905 - acc: 0.7562 - val_loss: 0.4765 - val_acc: 0.7655
Epoch 27/30

  1/102 [..............................] - ETA: 0s - loss: 0.5637 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.4843 - acc: 0.7548

102/102 [==============================] - 1s 6ms/step - loss: 0.4843 - acc: 0.7548 - val_loss: 0.4795 - val_acc: 0.7602
Epoch 28/30

  1/102 [..............................] - ETA: 0s - loss: 0.4582 - acc: 0.6800
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100/102 [============================>.] - ETA: 0s - loss: 0.4940 - acc: 0.7522
102/102 [==============================] - 0s 3ms/step - loss: 0.4918 - acc: 0.7530

102/102 [==============================] - 1s 6ms/step - loss: 0.4918 - acc: 0.7530 - val_loss: 0.4867 - val_acc: 0.7549
Epoch 29/30

  1/102 [..............................] - ETA: 0s - loss: 0.4472 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5046 - acc: 0.7306
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102/102 [==============================] - 0s 4ms/step - loss: 0.4841 - acc: 0.7542

102/102 [==============================] - 1s 6ms/step - loss: 0.4841 - acc: 0.7542 - val_loss: 0.4832 - val_acc: 0.7691
Epoch 30/30

  1/102 [..............................] - ETA: 0s - loss: 0.5634 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4826 - acc: 0.7775
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 53/102 [==============>...............] - ETA: 0s - loss: 0.4676 - acc: 0.7781
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102/102 [==============================] - 0s 3ms/step - loss: 0.4899 - acc: 0.7642

102/102 [==============================] - 1s 5ms/step - loss: 0.4899 - acc: 0.7642 - val_loss: 0.4911 - val_acc: 0.7549


Run completed: runs/2024-05-06T19-37-33Z

Training run 13/52 (flags = list(64, 32, 0.01, 20, 50, "sigmoid", "sigmoid", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-37-55Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 4:54 - loss: 0.3856 - acc: 0.8500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.


 18/254 [=>............................] - ETA: 0s - loss: 0.7371 - acc: 0.6889  
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254/254 [==============================] - 2s 3ms/step - loss: 0.6252 - acc: 0.6533

254/254 [==============================] - 3s 6ms/step - loss: 0.6252 - acc: 0.6533 - val_loss: 0.5183 - val_acc: 0.7584
Epoch 2/50

  1/254 [..............................] - ETA: 1s - loss: 0.4580 - acc: 0.9000
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143/254 [===============>..............] - ETA: 0s - loss: 0.5297 - acc: 0.7339
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254/254 [==============================] - 1s 3ms/step - loss: 0.5170 - acc: 0.7374

254/254 [==============================] - 1s 4ms/step - loss: 0.5170 - acc: 0.7374 - val_loss: 0.4675 - val_acc: 0.7709
Epoch 3/50

  1/254 [..............................] - ETA: 0s - loss: 0.3325 - acc: 0.8500
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253/254 [============================>.] - ETA: 0s - loss: 0.5034 - acc: 0.7451
254/254 [==============================] - 1s 3ms/step - loss: 0.5028 - acc: 0.7451

254/254 [==============================] - 1s 5ms/step - loss: 0.5028 - acc: 0.7451 - val_loss: 0.4798 - val_acc: 0.7655
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 0.3641 - acc: 0.8000
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240/254 [===========================>..] - ETA: 0s - loss: 0.5018 - acc: 0.7465
254/254 [==============================] - 1s 3ms/step - loss: 0.5007 - acc: 0.7469

254/254 [==============================] - 1s 4ms/step - loss: 0.5007 - acc: 0.7469 - val_loss: 0.4567 - val_acc: 0.7744
Epoch 5/50

  1/254 [..............................] - ETA: 3s - loss: 0.5666 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4942 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4942 - acc: 0.7510 - val_loss: 0.4924 - val_acc: 0.7602
Epoch 6/50

  1/254 [..............................] - ETA: 1s - loss: 0.3324 - acc: 0.8000
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238/254 [===========================>..] - ETA: 0s - loss: 0.4909 - acc: 0.7515
254/254 [==============================] - 1s 3ms/step - loss: 0.4895 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4895 - acc: 0.7510 - val_loss: 0.4821 - val_acc: 0.7620
Epoch 7/50

  1/254 [..............................] - ETA: 0s - loss: 0.5271 - acc: 0.6500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4909 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4909 - acc: 0.7542 - val_loss: 0.4773 - val_acc: 0.7620
Epoch 8/50

  1/254 [..............................] - ETA: 1s - loss: 0.3168 - acc: 0.8500
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184/254 [====================>.........] - ETA: 0s - loss: 0.4881 - acc: 0.7617
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254/254 [==============================] - 1s 3ms/step - loss: 0.4896 - acc: 0.7568

254/254 [==============================] - 1s 4ms/step - loss: 0.4896 - acc: 0.7568 - val_loss: 0.4890 - val_acc: 0.7567
Epoch 9/50

  1/254 [..............................] - ETA: 0s - loss: 0.5345 - acc: 0.6500
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148/254 [================>.............] - ETA: 0s - loss: 0.5021 - acc: 0.7429
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254/254 [==============================] - 1s 4ms/step - loss: 0.4851 - acc: 0.7581

254/254 [==============================] - 1s 5ms/step - loss: 0.4851 - acc: 0.7581 - val_loss: 0.4852 - val_acc: 0.7584
Epoch 10/50

  1/254 [..............................] - ETA: 0s - loss: 0.4960 - acc: 0.7000
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148/254 [================>.............] - ETA: 0s - loss: 0.4659 - acc: 0.7595
165/254 [==================>...........] - ETA: 0s - loss: 0.4642 - acc: 0.7621
184/254 [====================>.........] - ETA: 0s - loss: 0.4720 - acc: 0.7611
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254/254 [==============================] - 1s 3ms/step - loss: 0.4839 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4839 - acc: 0.7524 - val_loss: 0.5013 - val_acc: 0.7496
Epoch 11/50

  1/254 [..............................] - ETA: 0s - loss: 0.5245 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4862 - acc: 0.7518

254/254 [==============================] - 1s 4ms/step - loss: 0.4862 - acc: 0.7518 - val_loss: 0.4715 - val_acc: 0.7691
Epoch 12/50

  1/254 [..............................] - ETA: 0s - loss: 0.5817 - acc: 0.7500
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240/254 [===========================>..] - ETA: 0s - loss: 0.4821 - acc: 0.7527
254/254 [==============================] - 1s 4ms/step - loss: 0.4825 - acc: 0.7532

254/254 [==============================] - 1s 4ms/step - loss: 0.4825 - acc: 0.7532 - val_loss: 0.4763 - val_acc: 0.7620
Epoch 13/50

  1/254 [..............................] - ETA: 0s - loss: 0.4018 - acc: 0.9000
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148/254 [================>.............] - ETA: 0s - loss: 0.4856 - acc: 0.7510
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254/254 [==============================] - 1s 3ms/step - loss: 0.4863 - acc: 0.7506

254/254 [==============================] - 1s 4ms/step - loss: 0.4863 - acc: 0.7506 - val_loss: 0.4899 - val_acc: 0.7513
Epoch 14/50

  1/254 [..............................] - ETA: 1s - loss: 0.3793 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4841 - acc: 0.7552

254/254 [==============================] - 1s 5ms/step - loss: 0.4841 - acc: 0.7552 - val_loss: 0.4871 - val_acc: 0.7549


Run completed: runs/2024-05-06T19-37-55Z

Training run 14/52 (flags = list(32, 64, 0.001, 50, 50, "relu", "tanh", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-38-12Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:01 - loss: 0.7427 - acc: 0.5800

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.6933 - acc: 0.5550  
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102/102 [==============================] - 2s 3ms/step - loss: 0.5762 - acc: 0.6874

102/102 [==============================] - 2s 11ms/step - loss: 0.5762 - acc: 0.6874 - val_loss: 0.5117 - val_acc: 0.7300
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.5347 - acc: 0.7200
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102/102 [==============================] - 0s 4ms/step - loss: 0.5124 - acc: 0.7339

102/102 [==============================] - 1s 6ms/step - loss: 0.5124 - acc: 0.7339 - val_loss: 0.4802 - val_acc: 0.7602
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6931 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5521 - acc: 0.7294
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101/102 [============================>.] - ETA: 0s - loss: 0.5047 - acc: 0.7503
102/102 [==============================] - 0s 3ms/step - loss: 0.5043 - acc: 0.7504

102/102 [==============================] - 1s 6ms/step - loss: 0.5043 - acc: 0.7504 - val_loss: 0.4704 - val_acc: 0.7726
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.4490 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.5004 - acc: 0.7534

102/102 [==============================] - 1s 8ms/step - loss: 0.5004 - acc: 0.7534 - val_loss: 0.5006 - val_acc: 0.7407
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.6408 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4939 - acc: 0.7491

102/102 [==============================] - 1s 6ms/step - loss: 0.4939 - acc: 0.7491 - val_loss: 0.4885 - val_acc: 0.7496
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.4093 - acc: 0.8600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4968 - acc: 0.7445

102/102 [==============================] - 1s 5ms/step - loss: 0.4968 - acc: 0.7445 - val_loss: 0.4850 - val_acc: 0.7531
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.3772 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4903 - acc: 0.7485

102/102 [==============================] - 1s 6ms/step - loss: 0.4903 - acc: 0.7485 - val_loss: 0.4681 - val_acc: 0.7726
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.5462 - acc: 0.5800
 16/102 [===>..........................] - ETA: 0s - loss: 0.4897 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4887 - acc: 0.7510

102/102 [==============================] - 1s 5ms/step - loss: 0.4887 - acc: 0.7510 - val_loss: 0.5047 - val_acc: 0.7460
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.4169 - acc: 0.8000
 12/102 [==>...........................] - ETA: 0s - loss: 0.4780 - acc: 0.7517
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102/102 [==============================] - 0s 4ms/step - loss: 0.4849 - acc: 0.7520

102/102 [==============================] - 1s 6ms/step - loss: 0.4849 - acc: 0.7520 - val_loss: 0.4811 - val_acc: 0.7602
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4018 - acc: 0.8400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4980 - acc: 0.7541
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102/102 [==============================] - 0s 4ms/step - loss: 0.4853 - acc: 0.7516

102/102 [==============================] - 1s 8ms/step - loss: 0.4853 - acc: 0.7516 - val_loss: 0.4803 - val_acc: 0.7638
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.6054 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4469 - acc: 0.7741
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 85/102 [========================>.....] - ETA: 0s - loss: 0.4780 - acc: 0.7621
102/102 [==============================] - 0s 3ms/step - loss: 0.4790 - acc: 0.7599

102/102 [==============================] - 1s 6ms/step - loss: 0.4790 - acc: 0.7599 - val_loss: 0.4876 - val_acc: 0.7584
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.5200 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4768 - acc: 0.7518
 34/102 [=========>....................] - ETA: 0s - loss: 0.4883 - acc: 0.7559
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102/102 [==============================] - 0s 3ms/step - loss: 0.4801 - acc: 0.7593

102/102 [==============================] - 1s 7ms/step - loss: 0.4801 - acc: 0.7593 - val_loss: 0.4772 - val_acc: 0.7602
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.4232 - acc: 0.8600
 16/102 [===>..........................] - ETA: 0s - loss: 0.4528 - acc: 0.7688
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100/102 [============================>.] - ETA: 0s - loss: 0.4743 - acc: 0.7594
102/102 [==============================] - 0s 4ms/step - loss: 0.4753 - acc: 0.7579

102/102 [==============================] - 1s 6ms/step - loss: 0.4753 - acc: 0.7579 - val_loss: 0.4981 - val_acc: 0.7531
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.4657 - acc: 0.7600
 16/102 [===>..........................] - ETA: 0s - loss: 0.4718 - acc: 0.7375
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 52/102 [==============>...............] - ETA: 0s - loss: 0.4777 - acc: 0.7581
 70/102 [===================>..........] - ETA: 0s - loss: 0.4763 - acc: 0.7597
 86/102 [========================>.....] - ETA: 0s - loss: 0.4760 - acc: 0.7626
102/102 [==============================] - 0s 3ms/step - loss: 0.4763 - acc: 0.7631

102/102 [==============================] - 1s 5ms/step - loss: 0.4763 - acc: 0.7631 - val_loss: 0.5037 - val_acc: 0.7513
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.3436 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4585 - acc: 0.7563
 26/102 [======>.......................] - ETA: 0s - loss: 0.4619 - acc: 0.7477
 33/102 [========>.....................] - ETA: 0s - loss: 0.4605 - acc: 0.7606
 45/102 [============>.................] - ETA: 0s - loss: 0.4671 - acc: 0.7618
 62/102 [=================>............] - ETA: 0s - loss: 0.4729 - acc: 0.7590
 74/102 [====================>.........] - ETA: 0s - loss: 0.4734 - acc: 0.7551
 88/102 [========================>.....] - ETA: 0s - loss: 0.4671 - acc: 0.7582
102/102 [==============================] - 0s 4ms/step - loss: 0.4719 - acc: 0.7528

102/102 [==============================] - 1s 8ms/step - loss: 0.4719 - acc: 0.7528 - val_loss: 0.4781 - val_acc: 0.7638
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.4841 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4827 - acc: 0.7494
 35/102 [=========>....................] - ETA: 0s - loss: 0.4732 - acc: 0.7549
 53/102 [==============>...............] - ETA: 0s - loss: 0.4724 - acc: 0.7547
 70/102 [===================>..........] - ETA: 0s - loss: 0.4726 - acc: 0.7534
 88/102 [========================>.....] - ETA: 0s - loss: 0.4702 - acc: 0.7568
 94/102 [==========================>...] - ETA: 0s - loss: 0.4713 - acc: 0.7587
102/102 [==============================] - 0s 3ms/step - loss: 0.4730 - acc: 0.7583

102/102 [==============================] - 1s 6ms/step - loss: 0.4730 - acc: 0.7583 - val_loss: 0.5053 - val_acc: 0.7460
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.3370 - acc: 0.8200
 16/102 [===>..........................] - ETA: 0s - loss: 0.4323 - acc: 0.7750
 34/102 [=========>....................] - ETA: 0s - loss: 0.4558 - acc: 0.7706
 51/102 [==============>...............] - ETA: 0s - loss: 0.4516 - acc: 0.7733
 68/102 [===================>..........] - ETA: 0s - loss: 0.4634 - acc: 0.7724
 84/102 [=======================>......] - ETA: 0s - loss: 0.4659 - acc: 0.7698
100/102 [============================>.] - ETA: 0s - loss: 0.4721 - acc: 0.7614
102/102 [==============================] - 0s 3ms/step - loss: 0.4715 - acc: 0.7623

102/102 [==============================] - 1s 5ms/step - loss: 0.4715 - acc: 0.7623 - val_loss: 0.4901 - val_acc: 0.7513


Run completed: runs/2024-05-06T19-38-12Z

Training run 15/52 (flags = list(16, 32, 0.001, 50, 30, "sigmoid", "sigmoid", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-38-26Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:01 - loss: 1.1094 - acc: 0.2000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.


 18/102 [====>.........................] - ETA: 0s - loss: 1.0225 - acc: 0.2622  
 36/102 [=========>....................] - ETA: 0s - loss: 0.9310 - acc: 0.2883
 54/102 [==============>...............] - ETA: 0s - loss: 0.8901 - acc: 0.3200
 73/102 [====================>.........] - ETA: 0s - loss: 0.8490 - acc: 0.3518
 86/102 [========================>.....] - ETA: 0s - loss: 0.8278 - acc: 0.3758
100/102 [============================>.] - ETA: 0s - loss: 0.8151 - acc: 0.3958
102/102 [==============================] - 2s 3ms/step - loss: 0.8126 - acc: 0.3986

102/102 [==============================] - 2s 12ms/step - loss: 0.8126 - acc: 0.3986 - val_loss: 0.6687 - val_acc: 0.6963
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.6729 - acc: 0.5800
 15/102 [===>..........................] - ETA: 0s - loss: 0.6986 - acc: 0.5493
 33/102 [========>.....................] - ETA: 0s - loss: 0.6889 - acc: 0.5594
 52/102 [==============>...............] - ETA: 0s - loss: 0.6929 - acc: 0.5627
 63/102 [=================>............] - ETA: 0s - loss: 0.6860 - acc: 0.5641
 74/102 [====================>.........] - ETA: 0s - loss: 0.6848 - acc: 0.5657
 93/102 [==========================>...] - ETA: 0s - loss: 0.6811 - acc: 0.5697
102/102 [==============================] - 0s 4ms/step - loss: 0.6781 - acc: 0.5760

102/102 [==============================] - 1s 6ms/step - loss: 0.6781 - acc: 0.5760 - val_loss: 0.6102 - val_acc: 0.7371
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.6393 - acc: 0.6400
 15/102 [===>..........................] - ETA: 0s - loss: 0.6346 - acc: 0.6320
 32/102 [========>.....................] - ETA: 0s - loss: 0.6330 - acc: 0.6381
 38/102 [==========>...................] - ETA: 0s - loss: 0.6383 - acc: 0.6368
 57/102 [===============>..............] - ETA: 0s - loss: 0.6318 - acc: 0.6361
 76/102 [=====================>........] - ETA: 0s - loss: 0.6210 - acc: 0.6455
 95/102 [==========================>...] - ETA: 0s - loss: 0.6142 - acc: 0.6537
102/102 [==============================] - 0s 4ms/step - loss: 0.6140 - acc: 0.6560

102/102 [==============================] - 1s 7ms/step - loss: 0.6140 - acc: 0.6560 - val_loss: 0.5486 - val_acc: 0.7602
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.6843 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5670 - acc: 0.6833
 34/102 [=========>....................] - ETA: 0s - loss: 0.5603 - acc: 0.7059
 43/102 [===========>..................] - ETA: 0s - loss: 0.5717 - acc: 0.7102
 62/102 [=================>............] - ETA: 0s - loss: 0.5741 - acc: 0.7135
 80/102 [======================>.......] - ETA: 0s - loss: 0.5702 - acc: 0.7122
 95/102 [==========================>...] - ETA: 0s - loss: 0.5715 - acc: 0.7105
102/102 [==============================] - 0s 3ms/step - loss: 0.5751 - acc: 0.7081

102/102 [==============================] - 1s 6ms/step - loss: 0.5751 - acc: 0.7081 - val_loss: 0.5318 - val_acc: 0.7478
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.6743 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5398 - acc: 0.7222
 37/102 [=========>....................] - ETA: 0s - loss: 0.5426 - acc: 0.7270
 55/102 [===============>..............] - ETA: 0s - loss: 0.5430 - acc: 0.7327
 73/102 [====================>.........] - ETA: 0s - loss: 0.5572 - acc: 0.7268
 92/102 [==========================>...] - ETA: 0s - loss: 0.5615 - acc: 0.7191
102/102 [==============================] - 0s 3ms/step - loss: 0.5580 - acc: 0.7232

102/102 [==============================] - 1s 6ms/step - loss: 0.5580 - acc: 0.7232 - val_loss: 0.5125 - val_acc: 0.7638
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5040 - acc: 0.7400
 11/102 [==>...........................] - ETA: 0s - loss: 0.5130 - acc: 0.7527
 20/102 [====>.........................] - ETA: 0s - loss: 0.5241 - acc: 0.7460
 34/102 [=========>....................] - ETA: 0s - loss: 0.5288 - acc: 0.7465
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 67/102 [==================>...........] - ETA: 0s - loss: 0.5395 - acc: 0.7340
 83/102 [=======================>......] - ETA: 0s - loss: 0.5404 - acc: 0.7361
101/102 [============================>.] - ETA: 0s - loss: 0.5401 - acc: 0.7354
102/102 [==============================] - 0s 4ms/step - loss: 0.5400 - acc: 0.7353

102/102 [==============================] - 1s 6ms/step - loss: 0.5400 - acc: 0.7353 - val_loss: 0.5029 - val_acc: 0.7691
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5149 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5192 - acc: 0.7456
 36/102 [=========>....................] - ETA: 0s - loss: 0.5414 - acc: 0.7294
 51/102 [==============>...............] - ETA: 0s - loss: 0.5514 - acc: 0.7196
 69/102 [===================>..........] - ETA: 0s - loss: 0.5529 - acc: 0.7258
 87/102 [========================>.....] - ETA: 0s - loss: 0.5454 - acc: 0.7308
 92/102 [==========================>...] - ETA: 0s - loss: 0.5451 - acc: 0.7307
102/102 [==============================] - 0s 3ms/step - loss: 0.5393 - acc: 0.7347

102/102 [==============================] - 1s 6ms/step - loss: 0.5393 - acc: 0.7347 - val_loss: 0.4975 - val_acc: 0.7709
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5729 - acc: 0.6600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5335 - acc: 0.7333
 38/102 [==========>...................] - ETA: 0s - loss: 0.5415 - acc: 0.7426
 45/102 [============>.................] - ETA: 0s - loss: 0.5327 - acc: 0.7516
 57/102 [===============>..............] - ETA: 0s - loss: 0.5395 - acc: 0.7495
 76/102 [=====================>........] - ETA: 0s - loss: 0.5306 - acc: 0.7495
 91/102 [=========================>....] - ETA: 0s - loss: 0.5344 - acc: 0.7448
102/102 [==============================] - 0s 4ms/step - loss: 0.5362 - acc: 0.7459

102/102 [==============================] - 1s 6ms/step - loss: 0.5362 - acc: 0.7459 - val_loss: 0.4885 - val_acc: 0.7691
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4203 - acc: 0.8600
 13/102 [==>...........................] - ETA: 0s - loss: 0.4982 - acc: 0.7631
 21/102 [=====>........................] - ETA: 0s - loss: 0.4988 - acc: 0.7619
 34/102 [=========>....................] - ETA: 0s - loss: 0.5125 - acc: 0.7500
 52/102 [==============>...............] - ETA: 0s - loss: 0.5301 - acc: 0.7415
 70/102 [===================>..........] - ETA: 0s - loss: 0.5368 - acc: 0.7411
 82/102 [=======================>......] - ETA: 0s - loss: 0.5345 - acc: 0.7410
100/102 [============================>.] - ETA: 0s - loss: 0.5323 - acc: 0.7420
102/102 [==============================] - 0s 4ms/step - loss: 0.5325 - acc: 0.7416

102/102 [==============================] - 1s 7ms/step - loss: 0.5325 - acc: 0.7416 - val_loss: 0.4879 - val_acc: 0.7691
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.5297 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5280 - acc: 0.7556
 37/102 [=========>....................] - ETA: 0s - loss: 0.5160 - acc: 0.7519
 55/102 [===============>..............] - ETA: 0s - loss: 0.5120 - acc: 0.7542
 72/102 [====================>.........] - ETA: 0s - loss: 0.5143 - acc: 0.7514
 91/102 [=========================>....] - ETA: 0s - loss: 0.5181 - acc: 0.7495
101/102 [============================>.] - ETA: 0s - loss: 0.5169 - acc: 0.7487
102/102 [==============================] - 0s 3ms/step - loss: 0.5166 - acc: 0.7489

102/102 [==============================] - 1s 6ms/step - loss: 0.5166 - acc: 0.7489 - val_loss: 0.4987 - val_acc: 0.7638
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5031 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5120 - acc: 0.7576
 36/102 [=========>....................] - ETA: 0s - loss: 0.5325 - acc: 0.7383
 54/102 [==============>...............] - ETA: 0s - loss: 0.5287 - acc: 0.7463
 72/102 [====================>.........] - ETA: 0s - loss: 0.5192 - acc: 0.7522
 89/102 [=========================>....] - ETA: 0s - loss: 0.5160 - acc: 0.7524
102/102 [==============================] - 0s 3ms/step - loss: 0.5210 - acc: 0.7504

102/102 [==============================] - 1s 5ms/step - loss: 0.5210 - acc: 0.7504 - val_loss: 0.4864 - val_acc: 0.7655
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5972 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5082 - acc: 0.7578
 35/102 [=========>....................] - ETA: 0s - loss: 0.5404 - acc: 0.7320
 42/102 [===========>..................] - ETA: 0s - loss: 0.5365 - acc: 0.7333
 59/102 [================>.............] - ETA: 0s - loss: 0.5312 - acc: 0.7346
 76/102 [=====================>........] - ETA: 0s - loss: 0.5205 - acc: 0.7424
 92/102 [==========================>...] - ETA: 0s - loss: 0.5212 - acc: 0.7463
102/102 [==============================] - 0s 3ms/step - loss: 0.5210 - acc: 0.7471

102/102 [==============================] - 1s 6ms/step - loss: 0.5210 - acc: 0.7471 - val_loss: 0.4964 - val_acc: 0.7638
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.4942 - acc: 0.6400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5071 - acc: 0.7478
 38/102 [==========>...................] - ETA: 0s - loss: 0.5138 - acc: 0.7484
 55/102 [===============>..............] - ETA: 0s - loss: 0.5114 - acc: 0.7458
 74/102 [====================>.........] - ETA: 0s - loss: 0.5099 - acc: 0.7559
 92/102 [==========================>...] - ETA: 0s - loss: 0.5137 - acc: 0.7520
102/102 [==============================] - 0s 3ms/step - loss: 0.5162 - acc: 0.7542

102/102 [==============================] - 1s 6ms/step - loss: 0.5162 - acc: 0.7542 - val_loss: 0.4942 - val_acc: 0.7638
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.5185 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4938 - acc: 0.7622
 37/102 [=========>....................] - ETA: 0s - loss: 0.5002 - acc: 0.7584
 55/102 [===============>..............] - ETA: 0s - loss: 0.5066 - acc: 0.7578
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 84/102 [=======================>......] - ETA: 0s - loss: 0.5080 - acc: 0.7552
 98/102 [===========================>..] - ETA: 0s - loss: 0.5092 - acc: 0.7539
102/102 [==============================] - 0s 4ms/step - loss: 0.5127 - acc: 0.7516

102/102 [==============================] - 1s 6ms/step - loss: 0.5127 - acc: 0.7516 - val_loss: 0.4800 - val_acc: 0.7655
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.6613 - acc: 0.7400
 19/102 [====>.........................] - ETA: 0s - loss: 0.4972 - acc: 0.7716
 37/102 [=========>....................] - ETA: 0s - loss: 0.5222 - acc: 0.7524
 56/102 [===============>..............] - ETA: 0s - loss: 0.5227 - acc: 0.7471
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 85/102 [========================>.....] - ETA: 0s - loss: 0.5174 - acc: 0.7492
100/102 [============================>.] - ETA: 0s - loss: 0.5146 - acc: 0.7476
102/102 [==============================] - 0s 3ms/step - loss: 0.5138 - acc: 0.7479

102/102 [==============================] - 1s 6ms/step - loss: 0.5138 - acc: 0.7479 - val_loss: 0.4783 - val_acc: 0.7655
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.4857 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5307 - acc: 0.7711
 38/102 [==========>...................] - ETA: 0s - loss: 0.5077 - acc: 0.7742
 57/102 [===============>..............] - ETA: 0s - loss: 0.5141 - acc: 0.7621
 74/102 [====================>.........] - ETA: 0s - loss: 0.5093 - acc: 0.7595
 91/102 [=========================>....] - ETA: 0s - loss: 0.5091 - acc: 0.7554
102/102 [==============================] - 0s 3ms/step - loss: 0.5107 - acc: 0.7552

102/102 [==============================] - 1s 5ms/step - loss: 0.5107 - acc: 0.7552 - val_loss: 0.4848 - val_acc: 0.7655
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.6416 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5211 - acc: 0.7378
 37/102 [=========>....................] - ETA: 0s - loss: 0.5204 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.5171 - acc: 0.7487

102/102 [==============================] - 1s 6ms/step - loss: 0.5171 - acc: 0.7487 - val_loss: 0.4945 - val_acc: 0.7602
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4142 - acc: 0.8600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5065 - acc: 0.7633
 37/102 [=========>....................] - ETA: 0s - loss: 0.5103 - acc: 0.7676
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 91/102 [=========================>....] - ETA: 0s - loss: 0.5188 - acc: 0.7444
102/102 [==============================] - 0s 3ms/step - loss: 0.5200 - acc: 0.7479

102/102 [==============================] - 1s 5ms/step - loss: 0.5200 - acc: 0.7479 - val_loss: 0.4871 - val_acc: 0.7655
Epoch 19/30

  1/102 [..............................] - ETA: 0s - loss: 0.4711 - acc: 0.8200
  5/102 [>.............................] - ETA: 1s - loss: 0.5109 - acc: 0.7440
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102/102 [==============================] - 0s 4ms/step - loss: 0.5111 - acc: 0.7613

102/102 [==============================] - 1s 7ms/step - loss: 0.5111 - acc: 0.7613 - val_loss: 0.5060 - val_acc: 0.7584
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.5238 - acc: 0.8000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5179 - acc: 0.7389
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102/102 [==============================] - 0s 3ms/step - loss: 0.5058 - acc: 0.7477

102/102 [==============================] - 1s 6ms/step - loss: 0.5058 - acc: 0.7477 - val_loss: 0.4882 - val_acc: 0.7620
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.3883 - acc: 0.7600
 20/102 [====>.........................] - ETA: 0s - loss: 0.5054 - acc: 0.7500
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102/102 [==============================] - 0s 3ms/step - loss: 0.5064 - acc: 0.7502

102/102 [==============================] - 1s 5ms/step - loss: 0.5064 - acc: 0.7502 - val_loss: 0.4906 - val_acc: 0.7584
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.4545 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5141 - acc: 0.7389
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 76/102 [=====================>........] - ETA: 0s - loss: 0.5065 - acc: 0.7547
 93/102 [==========================>...] - ETA: 0s - loss: 0.5106 - acc: 0.7527
102/102 [==============================] - 0s 3ms/step - loss: 0.5078 - acc: 0.7524

102/102 [==============================] - 1s 6ms/step - loss: 0.5078 - acc: 0.7524 - val_loss: 0.4894 - val_acc: 0.7584
Epoch 23/30

  1/102 [..............................] - ETA: 0s - loss: 0.5039 - acc: 0.7400
 19/102 [====>.........................] - ETA: 0s - loss: 0.4917 - acc: 0.7653
 38/102 [==========>...................] - ETA: 0s - loss: 0.5027 - acc: 0.7611
 57/102 [===============>..............] - ETA: 0s - loss: 0.4950 - acc: 0.7632
 73/102 [====================>.........] - ETA: 0s - loss: 0.4927 - acc: 0.7605
 90/102 [=========================>....] - ETA: 0s - loss: 0.4966 - acc: 0.7611
102/102 [==============================] - 0s 3ms/step - loss: 0.5031 - acc: 0.7564

102/102 [==============================] - 1s 5ms/step - loss: 0.5031 - acc: 0.7564 - val_loss: 0.4880 - val_acc: 0.7584
Epoch 24/30

  1/102 [..............................] - ETA: 0s - loss: 0.5062 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4977 - acc: 0.7567
 33/102 [========>.....................] - ETA: 0s - loss: 0.5005 - acc: 0.7503
 42/102 [===========>..................] - ETA: 0s - loss: 0.4933 - acc: 0.7514
 55/102 [===============>..............] - ETA: 0s - loss: 0.5103 - acc: 0.7407
 73/102 [====================>.........] - ETA: 0s - loss: 0.5127 - acc: 0.7468
 91/102 [=========================>....] - ETA: 0s - loss: 0.5050 - acc: 0.7488
102/102 [==============================] - 0s 3ms/step - loss: 0.5056 - acc: 0.7495

102/102 [==============================] - 1s 6ms/step - loss: 0.5056 - acc: 0.7495 - val_loss: 0.4888 - val_acc: 0.7567
Epoch 25/30

  1/102 [..............................] - ETA: 0s - loss: 0.4475 - acc: 0.8000
 19/102 [====>.........................] - ETA: 0s - loss: 0.4854 - acc: 0.7547
 39/102 [==========>...................] - ETA: 0s - loss: 0.4979 - acc: 0.7579
 58/102 [================>.............] - ETA: 0s - loss: 0.5141 - acc: 0.7500
 77/102 [=====================>........] - ETA: 0s - loss: 0.5125 - acc: 0.7486
 84/102 [=======================>......] - ETA: 0s - loss: 0.5116 - acc: 0.7479
 91/102 [=========================>....] - ETA: 0s - loss: 0.5127 - acc: 0.7484
102/102 [==============================] - 0s 4ms/step - loss: 0.5059 - acc: 0.7502

102/102 [==============================] - 1s 7ms/step - loss: 0.5059 - acc: 0.7502 - val_loss: 0.4858 - val_acc: 0.7602


Run completed: runs/2024-05-06T19-38-26Z

Training run 16/52 (flags = list(64, 16, 0.01, 50, 30, "relu", "tanh", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-38-43Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:00 - loss: 0.7254 - acc: 0.6000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0044s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 11/102 [==>...........................] - ETA: 0s - loss: 0.6772 - acc: 0.6055  
 20/102 [====>.........................] - ETA: 0s - loss: 0.6734 - acc: 0.6050
 36/102 [=========>....................] - ETA: 0s - loss: 0.6582 - acc: 0.6256
 54/102 [==============>...............] - ETA: 0s - loss: 0.6334 - acc: 0.6422
 72/102 [====================>.........] - ETA: 0s - loss: 0.6144 - acc: 0.6597
 90/102 [=========================>....] - ETA: 0s - loss: 0.5981 - acc: 0.6702
102/102 [==============================] - 2s 4ms/step - loss: 0.5956 - acc: 0.6783

102/102 [==============================] - 2s 12ms/step - loss: 0.5956 - acc: 0.6783 - val_loss: 0.4843 - val_acc: 0.7673
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.5645 - acc: 0.7400
  7/102 [=>............................] - ETA: 0s - loss: 0.6143 - acc: 0.6771
 24/102 [======>.......................] - ETA: 0s - loss: 0.5609 - acc: 0.7125
 41/102 [===========>..................] - ETA: 0s - loss: 0.5285 - acc: 0.7327
 52/102 [==============>...............] - ETA: 0s - loss: 0.5262 - acc: 0.7381
 67/102 [==================>...........] - ETA: 0s - loss: 0.5264 - acc: 0.7406
 83/102 [=======================>......] - ETA: 0s - loss: 0.5242 - acc: 0.7405
 98/102 [===========================>..] - ETA: 0s - loss: 0.5251 - acc: 0.7420
102/102 [==============================] - 0s 4ms/step - loss: 0.5267 - acc: 0.7416

102/102 [==============================] - 1s 6ms/step - loss: 0.5267 - acc: 0.7416 - val_loss: 0.4871 - val_acc: 0.7655
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.3989 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4863 - acc: 0.7447
 34/102 [=========>....................] - ETA: 0s - loss: 0.4914 - acc: 0.7565
 51/102 [==============>...............] - ETA: 0s - loss: 0.5024 - acc: 0.7514
 68/102 [===================>..........] - ETA: 0s - loss: 0.5064 - acc: 0.7497
 84/102 [=======================>......] - ETA: 0s - loss: 0.5058 - acc: 0.7493
101/102 [============================>.] - ETA: 0s - loss: 0.5101 - acc: 0.7479
102/102 [==============================] - 0s 3ms/step - loss: 0.5093 - acc: 0.7485

102/102 [==============================] - 1s 6ms/step - loss: 0.5093 - acc: 0.7485 - val_loss: 0.4691 - val_acc: 0.7762
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.4258 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4805 - acc: 0.7733
 37/102 [=========>....................] - ETA: 0s - loss: 0.5016 - acc: 0.7492
 45/102 [============>.................] - ETA: 0s - loss: 0.5078 - acc: 0.7489
 54/102 [==============>...............] - ETA: 0s - loss: 0.5098 - acc: 0.7478
 65/102 [==================>...........] - ETA: 0s - loss: 0.5077 - acc: 0.7483
 78/102 [=====================>........] - ETA: 0s - loss: 0.5036 - acc: 0.7487
 90/102 [=========================>....] - ETA: 0s - loss: 0.5046 - acc: 0.7480
102/102 [==============================] - 0s 4ms/step - loss: 0.5065 - acc: 0.7532

102/102 [==============================] - 1s 8ms/step - loss: 0.5065 - acc: 0.7532 - val_loss: 0.4796 - val_acc: 0.7673
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.4370 - acc: 0.7600
 19/102 [====>.........................] - ETA: 0s - loss: 0.4965 - acc: 0.7674
 38/102 [==========>...................] - ETA: 0s - loss: 0.5151 - acc: 0.7521
 54/102 [==============>...............] - ETA: 0s - loss: 0.5094 - acc: 0.7456
 71/102 [===================>..........] - ETA: 0s - loss: 0.5012 - acc: 0.7451
 88/102 [========================>.....] - ETA: 0s - loss: 0.4982 - acc: 0.7495
102/102 [==============================] - 0s 3ms/step - loss: 0.4965 - acc: 0.7510

102/102 [==============================] - 1s 6ms/step - loss: 0.4965 - acc: 0.7510 - val_loss: 0.4745 - val_acc: 0.7638
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.4658 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5080 - acc: 0.7611
 37/102 [=========>....................] - ETA: 0s - loss: 0.4936 - acc: 0.7643
 55/102 [===============>..............] - ETA: 0s - loss: 0.4959 - acc: 0.7553
 73/102 [====================>.........] - ETA: 0s - loss: 0.4944 - acc: 0.7551
 90/102 [=========================>....] - ETA: 0s - loss: 0.4973 - acc: 0.7522
102/102 [==============================] - 0s 3ms/step - loss: 0.4937 - acc: 0.7542

102/102 [==============================] - 1s 5ms/step - loss: 0.4937 - acc: 0.7542 - val_loss: 0.4862 - val_acc: 0.7549
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5789 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4736 - acc: 0.7722
 36/102 [=========>....................] - ETA: 0s - loss: 0.4922 - acc: 0.7639
 50/102 [=============>................] - ETA: 0s - loss: 0.4879 - acc: 0.7648
 61/102 [================>.............] - ETA: 0s - loss: 0.4861 - acc: 0.7669
 76/102 [=====================>........] - ETA: 0s - loss: 0.4895 - acc: 0.7624
 85/102 [========================>.....] - ETA: 0s - loss: 0.4888 - acc: 0.7624
102/102 [==============================] - 0s 4ms/step - loss: 0.4933 - acc: 0.7585

102/102 [==============================] - 1s 6ms/step - loss: 0.4933 - acc: 0.7585 - val_loss: 0.4932 - val_acc: 0.7531
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.4219 - acc: 0.8600
 16/102 [===>..........................] - ETA: 0s - loss: 0.5028 - acc: 0.7462
 34/102 [=========>....................] - ETA: 0s - loss: 0.5161 - acc: 0.7482
 52/102 [==============>...............] - ETA: 0s - loss: 0.5084 - acc: 0.7415
 68/102 [===================>..........] - ETA: 0s - loss: 0.5041 - acc: 0.7415
 84/102 [=======================>......] - ETA: 0s - loss: 0.4956 - acc: 0.7488
101/102 [============================>.] - ETA: 0s - loss: 0.4892 - acc: 0.7503
102/102 [==============================] - 0s 3ms/step - loss: 0.4895 - acc: 0.7504

102/102 [==============================] - 1s 5ms/step - loss: 0.4895 - acc: 0.7504 - val_loss: 0.4665 - val_acc: 0.7709
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4562 - acc: 0.8200
 11/102 [==>...........................] - ETA: 0s - loss: 0.4671 - acc: 0.7836
 22/102 [=====>........................] - ETA: 0s - loss: 0.4783 - acc: 0.7627
 26/102 [======>.......................] - ETA: 0s - loss: 0.4848 - acc: 0.7662
 43/102 [===========>..................] - ETA: 0s - loss: 0.4910 - acc: 0.7614
 61/102 [================>.............] - ETA: 0s - loss: 0.4897 - acc: 0.7580
 76/102 [=====================>........] - ETA: 0s - loss: 0.4927 - acc: 0.7534
 94/102 [==========================>...] - ETA: 0s - loss: 0.4865 - acc: 0.7596
102/102 [==============================] - 0s 4ms/step - loss: 0.4886 - acc: 0.7564

102/102 [==============================] - 1s 7ms/step - loss: 0.4886 - acc: 0.7564 - val_loss: 0.4901 - val_acc: 0.7460
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.4595 - acc: 0.8200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4451 - acc: 0.7811
 36/102 [=========>....................] - ETA: 0s - loss: 0.4609 - acc: 0.7778
 53/102 [==============>...............] - ETA: 0s - loss: 0.4816 - acc: 0.7660
 71/102 [===================>..........] - ETA: 0s - loss: 0.4829 - acc: 0.7583
 77/102 [=====================>........] - ETA: 0s - loss: 0.4821 - acc: 0.7579
 94/102 [==========================>...] - ETA: 0s - loss: 0.4955 - acc: 0.7585
102/102 [==============================] - 0s 4ms/step - loss: 0.4922 - acc: 0.7575

102/102 [==============================] - 1s 7ms/step - loss: 0.4922 - acc: 0.7575 - val_loss: 0.5254 - val_acc: 0.7211
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.4349 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4869 - acc: 0.7624
 35/102 [=========>....................] - ETA: 0s - loss: 0.4961 - acc: 0.7526
 52/102 [==============>...............] - ETA: 0s - loss: 0.4932 - acc: 0.7488
 68/102 [===================>..........] - ETA: 0s - loss: 0.4910 - acc: 0.7524
 84/102 [=======================>......] - ETA: 0s - loss: 0.4875 - acc: 0.7550
101/102 [============================>.] - ETA: 0s - loss: 0.4863 - acc: 0.7566
102/102 [==============================] - 0s 3ms/step - loss: 0.4858 - acc: 0.7566

102/102 [==============================] - 1s 6ms/step - loss: 0.4858 - acc: 0.7566 - val_loss: 0.4916 - val_acc: 0.7531
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5212 - acc: 0.7400
 16/102 [===>..........................] - ETA: 0s - loss: 0.4744 - acc: 0.7575
 23/102 [=====>........................] - ETA: 0s - loss: 0.4783 - acc: 0.7600
 40/102 [==========>...................] - ETA: 0s - loss: 0.4851 - acc: 0.7550
 51/102 [==============>...............] - ETA: 0s - loss: 0.4825 - acc: 0.7596
 66/102 [==================>...........] - ETA: 0s - loss: 0.4847 - acc: 0.7621
 82/102 [=======================>......] - ETA: 0s - loss: 0.4806 - acc: 0.7612
 98/102 [===========================>..] - ETA: 0s - loss: 0.4841 - acc: 0.7612
102/102 [==============================] - 0s 4ms/step - loss: 0.4860 - acc: 0.7577

102/102 [==============================] - 1s 7ms/step - loss: 0.4860 - acc: 0.7577 - val_loss: 0.4970 - val_acc: 0.7496
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.5704 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4624 - acc: 0.7576
 34/102 [=========>....................] - ETA: 0s - loss: 0.4788 - acc: 0.7535
 51/102 [==============>...............] - ETA: 0s - loss: 0.4720 - acc: 0.7537
 69/102 [===================>..........] - ETA: 0s - loss: 0.4757 - acc: 0.7516
 85/102 [========================>.....] - ETA: 0s - loss: 0.4737 - acc: 0.7551
101/102 [============================>.] - ETA: 0s - loss: 0.4772 - acc: 0.7562
102/102 [==============================] - 0s 3ms/step - loss: 0.4774 - acc: 0.7562

102/102 [==============================] - 1s 6ms/step - loss: 0.4774 - acc: 0.7562 - val_loss: 0.4951 - val_acc: 0.7531
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.3975 - acc: 0.8000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4752 - acc: 0.7422
 37/102 [=========>....................] - ETA: 0s - loss: 0.4888 - acc: 0.7465
 54/102 [==============>...............] - ETA: 0s - loss: 0.4811 - acc: 0.7593
 71/102 [===================>..........] - ETA: 0s - loss: 0.4875 - acc: 0.7513
 88/102 [========================>.....] - ETA: 0s - loss: 0.4792 - acc: 0.7593
102/102 [==============================] - 0s 3ms/step - loss: 0.4798 - acc: 0.7581

102/102 [==============================] - 1s 5ms/step - loss: 0.4798 - acc: 0.7581 - val_loss: 0.4834 - val_acc: 0.7460
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.4015 - acc: 0.8600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4915 - acc: 0.7589
 37/102 [=========>....................] - ETA: 0s - loss: 0.4945 - acc: 0.7568
 52/102 [==============>...............] - ETA: 0s - loss: 0.4893 - acc: 0.7500
 68/102 [===================>..........] - ETA: 0s - loss: 0.4765 - acc: 0.7544
 84/102 [=======================>......] - ETA: 0s - loss: 0.4716 - acc: 0.7645
100/102 [============================>.] - ETA: 0s - loss: 0.4757 - acc: 0.7644
102/102 [==============================] - 0s 3ms/step - loss: 0.4766 - acc: 0.7646

102/102 [==============================] - 1s 7ms/step - loss: 0.4766 - acc: 0.7646 - val_loss: 0.5021 - val_acc: 0.7425
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.5982 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4772 - acc: 0.7556
 37/102 [=========>....................] - ETA: 0s - loss: 0.4748 - acc: 0.7578
 55/102 [===============>..............] - ETA: 0s - loss: 0.4686 - acc: 0.7618
 71/102 [===================>..........] - ETA: 0s - loss: 0.4711 - acc: 0.7597
 86/102 [========================>.....] - ETA: 0s - loss: 0.4745 - acc: 0.7633
102/102 [==============================] - 0s 3ms/step - loss: 0.4741 - acc: 0.7615

102/102 [==============================] - 1s 5ms/step - loss: 0.4741 - acc: 0.7615 - val_loss: 0.4826 - val_acc: 0.7602
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.4981 - acc: 0.7000
 19/102 [====>.........................] - ETA: 0s - loss: 0.4764 - acc: 0.7653
 37/102 [=========>....................] - ETA: 0s - loss: 0.4626 - acc: 0.7800
 53/102 [==============>...............] - ETA: 0s - loss: 0.4759 - acc: 0.7675
 69/102 [===================>..........] - ETA: 0s - loss: 0.4697 - acc: 0.7670
 83/102 [=======================>......] - ETA: 0s - loss: 0.4715 - acc: 0.7627
 97/102 [===========================>..] - ETA: 0s - loss: 0.4739 - acc: 0.7637
102/102 [==============================] - 0s 3ms/step - loss: 0.4749 - acc: 0.7648

102/102 [==============================] - 1s 6ms/step - loss: 0.4749 - acc: 0.7648 - val_loss: 0.4946 - val_acc: 0.7478
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4948 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4375 - acc: 0.7767
 36/102 [=========>....................] - ETA: 0s - loss: 0.4598 - acc: 0.7733
 53/102 [==============>...............] - ETA: 0s - loss: 0.4617 - acc: 0.7740
 69/102 [===================>..........] - ETA: 0s - loss: 0.4680 - acc: 0.7690
 88/102 [========================>.....] - ETA: 0s - loss: 0.4721 - acc: 0.7650
 95/102 [==========================>...] - ETA: 0s - loss: 0.4724 - acc: 0.7644
102/102 [==============================] - 0s 3ms/step - loss: 0.4725 - acc: 0.7642

102/102 [==============================] - 1s 6ms/step - loss: 0.4725 - acc: 0.7642 - val_loss: 0.4913 - val_acc: 0.7584


Run completed: runs/2024-05-06T19-38-43Z

Training run 17/52 (flags = list(16, 16, 0.01, 20, 50, "relu", "relu", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-38-57Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 6:34 - loss: 0.7752 - acc: 0.4500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.


 18/254 [=>............................] - ETA: 0s - loss: 0.6977 - acc: 0.5083  
 39/254 [===>..........................] - ETA: 0s - loss: 0.6742 - acc: 0.5064
 58/254 [=====>........................] - ETA: 0s - loss: 0.6645 - acc: 0.5483
 76/254 [=======>......................] - ETA: 0s - loss: 0.6563 - acc: 0.5645
 96/254 [==========>...................] - ETA: 0s - loss: 0.6395 - acc: 0.5927
115/254 [============>.................] - ETA: 0s - loss: 0.6300 - acc: 0.6065
134/254 [==============>...............] - ETA: 0s - loss: 0.6179 - acc: 0.6183
152/254 [================>.............] - ETA: 0s - loss: 0.6059 - acc: 0.6280
169/254 [==================>...........] - ETA: 0s - loss: 0.6043 - acc: 0.6352
189/254 [=====================>........] - ETA: 0s - loss: 0.5947 - acc: 0.6437
202/254 [======================>.......] - ETA: 0s - loss: 0.5895 - acc: 0.6485
207/254 [=======================>......] - ETA: 0s - loss: 0.5908 - acc: 0.6490
225/254 [=========================>....] - ETA: 0s - loss: 0.5896 - acc: 0.6511
236/254 [==========================>...] - ETA: 0s - loss: 0.5844 - acc: 0.6555
254/254 [==============================] - 2s 3ms/step - loss: 0.5794 - acc: 0.6598

254/254 [==============================] - 3s 7ms/step - loss: 0.5794 - acc: 0.6598 - val_loss: 0.4923 - val_acc: 0.7442
Epoch 2/50

  1/254 [..............................] - ETA: 1s - loss: 0.4998 - acc: 0.7000
 18/254 [=>............................] - ETA: 0s - loss: 0.5173 - acc: 0.7750
 35/254 [===>..........................] - ETA: 0s - loss: 0.5279 - acc: 0.7543
 53/254 [=====>........................] - ETA: 0s - loss: 0.5068 - acc: 0.7547
 66/254 [======>.......................] - ETA: 0s - loss: 0.5171 - acc: 0.7470
 83/254 [========>.....................] - ETA: 0s - loss: 0.5137 - acc: 0.7428
101/254 [==========>...................] - ETA: 0s - loss: 0.5039 - acc: 0.7446
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254/254 [==============================] - 1s 3ms/step - loss: 0.5145 - acc: 0.7408

254/254 [==============================] - 1s 4ms/step - loss: 0.5145 - acc: 0.7408 - val_loss: 0.4991 - val_acc: 0.7460
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5085 - acc: 0.7420

254/254 [==============================] - 1s 4ms/step - loss: 0.5085 - acc: 0.7420 - val_loss: 0.5018 - val_acc: 0.7442
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5051 - acc: 0.7435

254/254 [==============================] - 1s 5ms/step - loss: 0.5051 - acc: 0.7435 - val_loss: 0.5009 - val_acc: 0.7531
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4967 - acc: 0.7544

254/254 [==============================] - 1s 4ms/step - loss: 0.4967 - acc: 0.7544 - val_loss: 0.5043 - val_acc: 0.7407
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4947 - acc: 0.7465

254/254 [==============================] - 1s 4ms/step - loss: 0.4947 - acc: 0.7465 - val_loss: 0.4906 - val_acc: 0.7584
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4952 - acc: 0.7445

254/254 [==============================] - 1s 5ms/step - loss: 0.4952 - acc: 0.7445 - val_loss: 0.4913 - val_acc: 0.7549
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4925 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4925 - acc: 0.7542 - val_loss: 0.4966 - val_acc: 0.7567
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4897 - acc: 0.7534

254/254 [==============================] - 1s 4ms/step - loss: 0.4897 - acc: 0.7534 - val_loss: 0.4944 - val_acc: 0.7584
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4972 - acc: 0.7554

254/254 [==============================] - 1s 5ms/step - loss: 0.4972 - acc: 0.7554 - val_loss: 0.5046 - val_acc: 0.7442
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4822 - acc: 0.7548

254/254 [==============================] - 1s 4ms/step - loss: 0.4822 - acc: 0.7548 - val_loss: 0.4826 - val_acc: 0.7584
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4879 - acc: 0.7564

254/254 [==============================] - 1s 4ms/step - loss: 0.4879 - acc: 0.7564 - val_loss: 0.4885 - val_acc: 0.7602
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4808 - acc: 0.7583

254/254 [==============================] - 1s 4ms/step - loss: 0.4808 - acc: 0.7583 - val_loss: 0.4801 - val_acc: 0.7602
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4856 - acc: 0.7554

254/254 [==============================] - 1s 4ms/step - loss: 0.4856 - acc: 0.7554 - val_loss: 0.4909 - val_acc: 0.7513
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4852 - acc: 0.7613

254/254 [==============================] - 1s 4ms/step - loss: 0.4852 - acc: 0.7613 - val_loss: 0.4760 - val_acc: 0.7620
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4836 - acc: 0.7650

254/254 [==============================] - 1s 4ms/step - loss: 0.4836 - acc: 0.7650 - val_loss: 0.4949 - val_acc: 0.7478
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4855 - acc: 0.7607

254/254 [==============================] - 1s 4ms/step - loss: 0.4855 - acc: 0.7607 - val_loss: 0.4963 - val_acc: 0.7496
Epoch 18/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4803 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4803 - acc: 0.7575 - val_loss: 0.4778 - val_acc: 0.7602
Epoch 19/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4831 - acc: 0.7633

254/254 [==============================] - 1s 4ms/step - loss: 0.4831 - acc: 0.7633 - val_loss: 0.4803 - val_acc: 0.7584
Epoch 20/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4825 - acc: 0.7656

254/254 [==============================] - 1s 4ms/step - loss: 0.4825 - acc: 0.7656 - val_loss: 0.4950 - val_acc: 0.7496
Epoch 21/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4794 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4794 - acc: 0.7556 - val_loss: 0.4796 - val_acc: 0.7726
Epoch 22/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4836 - acc: 0.7619

254/254 [==============================] - 1s 4ms/step - loss: 0.4836 - acc: 0.7619 - val_loss: 0.4821 - val_acc: 0.7673
Epoch 23/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4820 - acc: 0.7688 - val_loss: 0.4773 - val_acc: 0.7744
Epoch 24/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4775 - acc: 0.7666

254/254 [==============================] - 1s 4ms/step - loss: 0.4775 - acc: 0.7666 - val_loss: 0.4847 - val_acc: 0.7584
Epoch 25/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4761 - acc: 0.7633

254/254 [==============================] - 1s 4ms/step - loss: 0.4761 - acc: 0.7633 - val_loss: 0.4781 - val_acc: 0.7567


Run completed: runs/2024-05-06T19-38-57Z

Training run 18/52 (flags = list(16, 32, 0.001, 20, 50, "relu", "tanh", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-39-26Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0290s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5978 - acc: 0.6716

254/254 [==============================] - 3s 7ms/step - loss: 0.5978 - acc: 0.6716 - val_loss: 0.5173 - val_acc: 0.7336
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5409 - acc: 0.7301

254/254 [==============================] - 1s 4ms/step - loss: 0.5409 - acc: 0.7301 - val_loss: 0.5141 - val_acc: 0.7336
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5189 - acc: 0.7481

254/254 [==============================] - 1s 5ms/step - loss: 0.5189 - acc: 0.7481 - val_loss: 0.5041 - val_acc: 0.7531
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5175 - acc: 0.7447

254/254 [==============================] - 1s 4ms/step - loss: 0.5175 - acc: 0.7447 - val_loss: 0.4816 - val_acc: 0.7691
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5093 - acc: 0.7499

254/254 [==============================] - 1s 4ms/step - loss: 0.5093 - acc: 0.7499 - val_loss: 0.4952 - val_acc: 0.7549
Epoch 6/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5037 - acc: 0.7534

254/254 [==============================] - 1s 5ms/step - loss: 0.5037 - acc: 0.7534 - val_loss: 0.5028 - val_acc: 0.7442
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4996 - acc: 0.7500

254/254 [==============================] - 1s 4ms/step - loss: 0.4996 - acc: 0.7500 - val_loss: 0.4911 - val_acc: 0.7531
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4969 - acc: 0.7520

254/254 [==============================] - 1s 4ms/step - loss: 0.4969 - acc: 0.7520 - val_loss: 0.4994 - val_acc: 0.7531
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4958 - acc: 0.7597

254/254 [==============================] - 1s 4ms/step - loss: 0.4958 - acc: 0.7597 - val_loss: 0.4793 - val_acc: 0.7638
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4955 - acc: 0.7637

254/254 [==============================] - 1s 4ms/step - loss: 0.4955 - acc: 0.7637 - val_loss: 0.5013 - val_acc: 0.7531
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4984 - acc: 0.7595

254/254 [==============================] - 1s 4ms/step - loss: 0.4984 - acc: 0.7595 - val_loss: 0.4864 - val_acc: 0.7602
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4937 - acc: 0.7664

254/254 [==============================] - 1s 4ms/step - loss: 0.4937 - acc: 0.7664 - val_loss: 0.5036 - val_acc: 0.7425
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4955 - acc: 0.7642

254/254 [==============================] - 1s 4ms/step - loss: 0.4955 - acc: 0.7642 - val_loss: 0.4912 - val_acc: 0.7584
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4953 - acc: 0.7652

254/254 [==============================] - 1s 5ms/step - loss: 0.4953 - acc: 0.7652 - val_loss: 0.4866 - val_acc: 0.7620
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4915 - acc: 0.7605

254/254 [==============================] - 1s 4ms/step - loss: 0.4915 - acc: 0.7605 - val_loss: 0.4848 - val_acc: 0.7655
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4867 - acc: 0.7692

254/254 [==============================] - 1s 4ms/step - loss: 0.4867 - acc: 0.7692 - val_loss: 0.4869 - val_acc: 0.7655
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4909 - acc: 0.7650

254/254 [==============================] - 1s 4ms/step - loss: 0.4909 - acc: 0.7650 - val_loss: 0.5036 - val_acc: 0.7460
Epoch 18/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4835 - acc: 0.7654

254/254 [==============================] - 1s 4ms/step - loss: 0.4835 - acc: 0.7654 - val_loss: 0.4938 - val_acc: 0.7407
Epoch 19/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4933 - acc: 0.7660

254/254 [==============================] - 1s 4ms/step - loss: 0.4933 - acc: 0.7660 - val_loss: 0.4953 - val_acc: 0.7513


Run completed: runs/2024-05-06T19-39-26Z

Training run 19/52 (flags = list(64, 64, 0.01, 20, 30, "sigmoid", "relu", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-39-49Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:03 - loss: 0.6671 - acc: 0.6500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0285s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0285s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6081 - acc: 0.6523

254/254 [==============================] - 3s 7ms/step - loss: 0.6081 - acc: 0.6523 - val_loss: 0.4657 - val_acc: 0.7744
Epoch 2/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5350 - acc: 0.7276

254/254 [==============================] - 1s 5ms/step - loss: 0.5350 - acc: 0.7276 - val_loss: 0.4830 - val_acc: 0.7673
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5299 - acc: 0.7339

254/254 [==============================] - 1s 4ms/step - loss: 0.5299 - acc: 0.7339 - val_loss: 0.5204 - val_acc: 0.7513
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5122 - acc: 0.7398

254/254 [==============================] - 1s 4ms/step - loss: 0.5122 - acc: 0.7398 - val_loss: 0.4774 - val_acc: 0.7709
Epoch 5/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.5050 - acc: 0.7502 - val_loss: 0.4841 - val_acc: 0.7620
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5070 - acc: 0.7392

254/254 [==============================] - 1s 4ms/step - loss: 0.5070 - acc: 0.7392 - val_loss: 0.4779 - val_acc: 0.7726
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4991 - acc: 0.7500

254/254 [==============================] - 1s 4ms/step - loss: 0.4991 - acc: 0.7500 - val_loss: 0.4755 - val_acc: 0.7744
Epoch 8/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4978 - acc: 0.7493

254/254 [==============================] - 1s 5ms/step - loss: 0.4978 - acc: 0.7493 - val_loss: 0.5095 - val_acc: 0.7531
Epoch 9/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4979 - acc: 0.7467 - val_loss: 0.4910 - val_acc: 0.7655
Epoch 10/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4953 - acc: 0.7465 - val_loss: 0.4841 - val_acc: 0.7709
Epoch 11/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4912 - acc: 0.7546 - val_loss: 0.4691 - val_acc: 0.7780


Run completed: runs/2024-05-06T19-39-49Z

Training run 20/52 (flags = list(32, 32, 0.001, 20, 30, "relu", "sigmoid", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-40-03Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5985 - acc: 0.6645

254/254 [==============================] - 3s 7ms/step - loss: 0.5985 - acc: 0.6645 - val_loss: 0.4906 - val_acc: 0.7567
Epoch 2/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5315 - acc: 0.7428

254/254 [==============================] - 1s 4ms/step - loss: 0.5315 - acc: 0.7428 - val_loss: 0.4990 - val_acc: 0.7620
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5165 - acc: 0.7443

254/254 [==============================] - 1s 4ms/step - loss: 0.5165 - acc: 0.7443 - val_loss: 0.4791 - val_acc: 0.7638
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5103 - acc: 0.7528

254/254 [==============================] - 1s 4ms/step - loss: 0.5103 - acc: 0.7528 - val_loss: 0.4937 - val_acc: 0.7584
Epoch 5/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5112 - acc: 0.7512

254/254 [==============================] - 1s 4ms/step - loss: 0.5112 - acc: 0.7512 - val_loss: 0.4879 - val_acc: 0.7602
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5036 - acc: 0.7483

254/254 [==============================] - 1s 4ms/step - loss: 0.5036 - acc: 0.7483 - val_loss: 0.4776 - val_acc: 0.7638
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5025 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.5025 - acc: 0.7493 - val_loss: 0.4755 - val_acc: 0.7602
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4990 - acc: 0.7530

254/254 [==============================] - 1s 4ms/step - loss: 0.4990 - acc: 0.7530 - val_loss: 0.4719 - val_acc: 0.7673
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4984 - acc: 0.7447

254/254 [==============================] - 1s 4ms/step - loss: 0.4984 - acc: 0.7447 - val_loss: 0.4729 - val_acc: 0.7620
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4998 - acc: 0.7528

254/254 [==============================] - 1s 4ms/step - loss: 0.4998 - acc: 0.7528 - val_loss: 0.5104 - val_acc: 0.7478
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4967 - acc: 0.7477

254/254 [==============================] - 1s 4ms/step - loss: 0.4967 - acc: 0.7477 - val_loss: 0.4783 - val_acc: 0.7567
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4928 - acc: 0.7479

254/254 [==============================] - 1s 4ms/step - loss: 0.4928 - acc: 0.7479 - val_loss: 0.4868 - val_acc: 0.7531
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4859 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4859 - acc: 0.7510 - val_loss: 0.4775 - val_acc: 0.7620
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4850 - acc: 0.7611

254/254 [==============================] - 1s 4ms/step - loss: 0.4850 - acc: 0.7611 - val_loss: 0.4969 - val_acc: 0.7567
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4899 - acc: 0.7457

254/254 [==============================] - 1s 4ms/step - loss: 0.4899 - acc: 0.7457 - val_loss: 0.4795 - val_acc: 0.7584
Epoch 16/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4902 - acc: 0.7522

254/254 [==============================] - 1s 4ms/step - loss: 0.4902 - acc: 0.7522 - val_loss: 0.4677 - val_acc: 0.7744
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4869 - acc: 0.7560

254/254 [==============================] - 1s 4ms/step - loss: 0.4869 - acc: 0.7560 - val_loss: 0.4697 - val_acc: 0.7762
Epoch 18/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4888 - acc: 0.7453

254/254 [==============================] - 1s 4ms/step - loss: 0.4888 - acc: 0.7453 - val_loss: 0.4759 - val_acc: 0.7638
Epoch 19/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4853 - acc: 0.7497

254/254 [==============================] - 1s 4ms/step - loss: 0.4853 - acc: 0.7497 - val_loss: 0.4798 - val_acc: 0.7655
Epoch 20/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4836 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4836 - acc: 0.7575 - val_loss: 0.4989 - val_acc: 0.7407
Epoch 21/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4830 - acc: 0.7532 - val_loss: 0.4792 - val_acc: 0.7567
Epoch 22/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4809 - acc: 0.7536 - val_loss: 0.4731 - val_acc: 0.7691
Epoch 23/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4853 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4853 - acc: 0.7524 - val_loss: 0.4807 - val_acc: 0.7638
Epoch 24/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4798 - acc: 0.7560

254/254 [==============================] - 1s 4ms/step - loss: 0.4798 - acc: 0.7560 - val_loss: 0.4626 - val_acc: 0.7780
Epoch 25/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4812 - acc: 0.7587

254/254 [==============================] - 1s 4ms/step - loss: 0.4812 - acc: 0.7587 - val_loss: 0.4643 - val_acc: 0.7691
Epoch 26/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4786 - acc: 0.7607

254/254 [==============================] - 1s 5ms/step - loss: 0.4786 - acc: 0.7607 - val_loss: 0.4971 - val_acc: 0.7567
Epoch 27/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4800 - acc: 0.7518

254/254 [==============================] - 1s 4ms/step - loss: 0.4800 - acc: 0.7518 - val_loss: 0.4772 - val_acc: 0.7655
Epoch 28/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4782 - acc: 0.7571

254/254 [==============================] - 1s 5ms/step - loss: 0.4782 - acc: 0.7571 - val_loss: 0.4592 - val_acc: 0.7709
Epoch 29/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4800 - acc: 0.7532

254/254 [==============================] - 1s 4ms/step - loss: 0.4800 - acc: 0.7532 - val_loss: 0.4777 - val_acc: 0.7673
Epoch 30/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4838 - acc: 0.7522

254/254 [==============================] - 1s 4ms/step - loss: 0.4838 - acc: 0.7522 - val_loss: 0.4912 - val_acc: 0.7584


Run completed: runs/2024-05-06T19-40-03Z

Training run 21/52 (flags = list(32, 64, 0.001, 20, 50, "relu", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-40-38Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5400 - acc: 0.7357

254/254 [==============================] - 3s 6ms/step - loss: 0.5400 - acc: 0.7357 - val_loss: 0.5080 - val_acc: 0.7336
Epoch 2/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5061 - acc: 0.7418

254/254 [==============================] - 1s 4ms/step - loss: 0.5061 - acc: 0.7418 - val_loss: 0.4543 - val_acc: 0.7798
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4986 - acc: 0.7435

254/254 [==============================] - 1s 5ms/step - loss: 0.4986 - acc: 0.7435 - val_loss: 0.4813 - val_acc: 0.7549
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4938 - acc: 0.7512

254/254 [==============================] - 1s 4ms/step - loss: 0.4938 - acc: 0.7512 - val_loss: 0.4680 - val_acc: 0.7744
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4852 - acc: 0.7443

254/254 [==============================] - 1s 4ms/step - loss: 0.4852 - acc: 0.7443 - val_loss: 0.4736 - val_acc: 0.7620
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4850 - acc: 0.7491

254/254 [==============================] - 1s 4ms/step - loss: 0.4850 - acc: 0.7491 - val_loss: 0.4669 - val_acc: 0.7709
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4858 - acc: 0.7504

254/254 [==============================] - 1s 4ms/step - loss: 0.4858 - acc: 0.7504 - val_loss: 0.4808 - val_acc: 0.7691
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4789 - acc: 0.7534

254/254 [==============================] - 1s 4ms/step - loss: 0.4789 - acc: 0.7534 - val_loss: 0.4918 - val_acc: 0.7442
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4740 - acc: 0.7583

254/254 [==============================] - 1s 4ms/step - loss: 0.4740 - acc: 0.7583 - val_loss: 0.4692 - val_acc: 0.7691
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4768 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4768 - acc: 0.7542 - val_loss: 0.4988 - val_acc: 0.7531
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4717 - acc: 0.7562

254/254 [==============================] - 1s 4ms/step - loss: 0.4717 - acc: 0.7562 - val_loss: 0.4823 - val_acc: 0.7584
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4707 - acc: 0.7591

254/254 [==============================] - 1s 4ms/step - loss: 0.4707 - acc: 0.7591 - val_loss: 0.4887 - val_acc: 0.7460


Run completed: runs/2024-05-06T19-40-38Z

Training run 22/52 (flags = list(32, 64, 0.01, 50, 30, "sigmoid", "tanh", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-40-53Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:38 - loss: 0.8072 - acc: 0.5000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0963s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0963s). Check your callbacks.


 19/102 [====>.........................] - ETA: 0s - loss: 0.7432 - acc: 0.4432  
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102/102 [==============================] - 2s 3ms/step - loss: 0.6354 - acc: 0.6253

102/102 [==============================] - 3s 12ms/step - loss: 0.6354 - acc: 0.6253 - val_loss: 0.4864 - val_acc: 0.7851
Epoch 2/30

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102/102 [==============================] - 0s 4ms/step - loss: 0.5345 - acc: 0.7305

102/102 [==============================] - 1s 7ms/step - loss: 0.5345 - acc: 0.7305 - val_loss: 0.5213 - val_acc: 0.7531
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.5700 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.5225 - acc: 0.7416

102/102 [==============================] - 1s 6ms/step - loss: 0.5225 - acc: 0.7416 - val_loss: 0.4930 - val_acc: 0.7638
Epoch 4/30

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102/102 [==============================] - 0s 3ms/step - loss: 0.5064 - acc: 0.7500

102/102 [==============================] - 1s 6ms/step - loss: 0.5064 - acc: 0.7500 - val_loss: 0.4849 - val_acc: 0.7655
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.7262 - acc: 0.6600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5356 - acc: 0.7244
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102/102 [==============================] - 0s 3ms/step - loss: 0.5120 - acc: 0.7424

102/102 [==============================] - 1s 5ms/step - loss: 0.5120 - acc: 0.7424 - val_loss: 0.4763 - val_acc: 0.7673
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.6241 - acc: 0.6800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5306 - acc: 0.7462
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102/102 [==============================] - 0s 3ms/step - loss: 0.5043 - acc: 0.7556

102/102 [==============================] - 1s 6ms/step - loss: 0.5043 - acc: 0.7556 - val_loss: 0.4900 - val_acc: 0.7620
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.4905 - acc: 0.8200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5080 - acc: 0.7478
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102/102 [==============================] - 0s 4ms/step - loss: 0.5043 - acc: 0.7532

102/102 [==============================] - 1s 6ms/step - loss: 0.5043 - acc: 0.7532 - val_loss: 0.4731 - val_acc: 0.7691
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5594 - acc: 0.8400
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102/102 [==============================] - 0s 3ms/step - loss: 0.5019 - acc: 0.7528

102/102 [==============================] - 1s 5ms/step - loss: 0.5019 - acc: 0.7528 - val_loss: 0.4725 - val_acc: 0.7691
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.5385 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4834 - acc: 0.7741
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102/102 [==============================] - 0s 4ms/step - loss: 0.4957 - acc: 0.7534

102/102 [==============================] - 1s 7ms/step - loss: 0.4957 - acc: 0.7534 - val_loss: 0.4758 - val_acc: 0.7638
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.4529 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4802 - acc: 0.7553
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 83/102 [=======================>......] - ETA: 0s - loss: 0.5000 - acc: 0.7477
 99/102 [============================>.] - ETA: 0s - loss: 0.4959 - acc: 0.7499
102/102 [==============================] - 0s 3ms/step - loss: 0.4972 - acc: 0.7497

102/102 [==============================] - 1s 6ms/step - loss: 0.4972 - acc: 0.7497 - val_loss: 0.4896 - val_acc: 0.7602
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.3523 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4948 - acc: 0.7485

102/102 [==============================] - 1s 5ms/step - loss: 0.4948 - acc: 0.7485 - val_loss: 0.5059 - val_acc: 0.7460
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.4786 - acc: 0.6200
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102/102 [==============================] - 0s 3ms/step - loss: 0.4950 - acc: 0.7512

102/102 [==============================] - 1s 6ms/step - loss: 0.4950 - acc: 0.7512 - val_loss: 0.4853 - val_acc: 0.7584
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.5353 - acc: 0.8000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4961 - acc: 0.7508

102/102 [==============================] - 1s 5ms/step - loss: 0.4961 - acc: 0.7508 - val_loss: 0.4771 - val_acc: 0.7638
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.7709 - acc: 0.7000
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102/102 [==============================] - 0s 4ms/step - loss: 0.4918 - acc: 0.7516

102/102 [==============================] - 1s 8ms/step - loss: 0.4918 - acc: 0.7516 - val_loss: 0.4814 - val_acc: 0.7620
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5065 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4908 - acc: 0.7516

102/102 [==============================] - 1s 6ms/step - loss: 0.4908 - acc: 0.7516 - val_loss: 0.4906 - val_acc: 0.7567
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.5537 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4974 - acc: 0.7433
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102/102 [==============================] - 0s 3ms/step - loss: 0.4926 - acc: 0.7526

102/102 [==============================] - 1s 5ms/step - loss: 0.4926 - acc: 0.7526 - val_loss: 0.4889 - val_acc: 0.7567
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.7323 - acc: 0.5800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5063 - acc: 0.7412
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 70/102 [===================>..........] - ETA: 0s - loss: 0.4946 - acc: 0.7517
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102/102 [==============================] - 0s 3ms/step - loss: 0.4909 - acc: 0.7524

102/102 [==============================] - 1s 6ms/step - loss: 0.4909 - acc: 0.7524 - val_loss: 0.4847 - val_acc: 0.7620
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.5435 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5182 - acc: 0.7306
 35/102 [=========>....................] - ETA: 0s - loss: 0.4834 - acc: 0.7486
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102/102 [==============================] - 0s 3ms/step - loss: 0.4907 - acc: 0.7532

102/102 [==============================] - 1s 6ms/step - loss: 0.4907 - acc: 0.7532 - val_loss: 0.4880 - val_acc: 0.7549


Run completed: runs/2024-05-06T19-40-53Z

Training run 23/52 (flags = list(64, 64, 0.001, 20, 50, "relu", "tanh", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-41-07Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:02 - loss: 0.6536 - acc: 0.4000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0080s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0080s vs `on_train_batch_end` time: 0.0295s). Check your callbacks.


  8/254 [..............................] - ETA: 1s - loss: 0.7258 - acc: 0.5188  
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235/254 [==========================>...] - ETA: 0s - loss: 0.5867 - acc: 0.6930
254/254 [==============================] - 2s 3ms/step - loss: 0.5842 - acc: 0.6978

254/254 [==============================] - 3s 7ms/step - loss: 0.5842 - acc: 0.6978 - val_loss: 0.4926 - val_acc: 0.7567
Epoch 2/50

  1/254 [..............................] - ETA: 0s - loss: 0.5108 - acc: 0.7000
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249/254 [============================>.] - ETA: 0s - loss: 0.5170 - acc: 0.7373
254/254 [==============================] - 1s 3ms/step - loss: 0.5177 - acc: 0.7372

254/254 [==============================] - 1s 4ms/step - loss: 0.5177 - acc: 0.7372 - val_loss: 0.4965 - val_acc: 0.7425
Epoch 3/50

  1/254 [..............................] - ETA: 0s - loss: 0.5664 - acc: 0.8500
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247/254 [============================>.] - ETA: 0s - loss: 0.5084 - acc: 0.7415
254/254 [==============================] - 1s 4ms/step - loss: 0.5093 - acc: 0.7422

254/254 [==============================] - 1s 5ms/step - loss: 0.5093 - acc: 0.7422 - val_loss: 0.5062 - val_acc: 0.7336
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 0.4101 - acc: 0.8000
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235/254 [==========================>...] - ETA: 0s - loss: 0.5084 - acc: 0.7343
252/254 [============================>.] - ETA: 0s - loss: 0.5050 - acc: 0.7355
254/254 [==============================] - 1s 3ms/step - loss: 0.5056 - acc: 0.7359

254/254 [==============================] - 1s 4ms/step - loss: 0.5056 - acc: 0.7359 - val_loss: 0.4816 - val_acc: 0.7620
Epoch 5/50

  1/254 [..............................] - ETA: 1s - loss: 0.2309 - acc: 0.9500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4961 - acc: 0.7408

254/254 [==============================] - 1s 5ms/step - loss: 0.4961 - acc: 0.7408 - val_loss: 0.4791 - val_acc: 0.7638
Epoch 6/50

  1/254 [..............................] - ETA: 0s - loss: 0.3736 - acc: 0.8500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4884 - acc: 0.7465

254/254 [==============================] - 1s 4ms/step - loss: 0.4884 - acc: 0.7465 - val_loss: 0.4928 - val_acc: 0.7460
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4875 - acc: 0.7540

254/254 [==============================] - 1s 4ms/step - loss: 0.4875 - acc: 0.7540 - val_loss: 0.4958 - val_acc: 0.7425
Epoch 8/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4911 - acc: 0.7495

254/254 [==============================] - 1s 5ms/step - loss: 0.4911 - acc: 0.7495 - val_loss: 0.4949 - val_acc: 0.7567
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4837 - acc: 0.7554

254/254 [==============================] - 1s 4ms/step - loss: 0.4837 - acc: 0.7554 - val_loss: 0.5103 - val_acc: 0.7584
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4813 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.4813 - acc: 0.7514 - val_loss: 0.4945 - val_acc: 0.7549
Epoch 11/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4806 - acc: 0.7571

254/254 [==============================] - 1s 5ms/step - loss: 0.4806 - acc: 0.7571 - val_loss: 0.4794 - val_acc: 0.7691
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4722 - acc: 0.7522

254/254 [==============================] - 1s 4ms/step - loss: 0.4722 - acc: 0.7522 - val_loss: 0.4767 - val_acc: 0.7638
Epoch 13/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4783 - acc: 0.7542

254/254 [==============================] - 1s 5ms/step - loss: 0.4783 - acc: 0.7542 - val_loss: 0.5033 - val_acc: 0.7513
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4809 - acc: 0.7475

254/254 [==============================] - 1s 4ms/step - loss: 0.4809 - acc: 0.7475 - val_loss: 0.4866 - val_acc: 0.7549
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4727 - acc: 0.7526

254/254 [==============================] - 1s 4ms/step - loss: 0.4727 - acc: 0.7526 - val_loss: 0.4841 - val_acc: 0.7726
Epoch 16/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4784 - acc: 0.7536

254/254 [==============================] - 1s 5ms/step - loss: 0.4784 - acc: 0.7536 - val_loss: 0.4911 - val_acc: 0.7673
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4742 - acc: 0.7504

254/254 [==============================] - 1s 4ms/step - loss: 0.4742 - acc: 0.7504 - val_loss: 0.4809 - val_acc: 0.7744
Epoch 18/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4755 - acc: 0.7499

254/254 [==============================] - 1s 4ms/step - loss: 0.4755 - acc: 0.7499 - val_loss: 0.4913 - val_acc: 0.7638
Epoch 19/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4673 - acc: 0.7609

254/254 [==============================] - 1s 5ms/step - loss: 0.4673 - acc: 0.7609 - val_loss: 0.4800 - val_acc: 0.7620
Epoch 20/50

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254/254 [==============================] - 1s 5ms/step - loss: 0.4759 - acc: 0.7569

254/254 [==============================] - 2s 6ms/step - loss: 0.4759 - acc: 0.7569 - val_loss: 0.5017 - val_acc: 0.7567
Epoch 21/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4739 - acc: 0.7558

254/254 [==============================] - 1s 5ms/step - loss: 0.4739 - acc: 0.7558 - val_loss: 0.4854 - val_acc: 0.7638
Epoch 22/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4701 - acc: 0.7562

254/254 [==============================] - 1s 4ms/step - loss: 0.4701 - acc: 0.7562 - val_loss: 0.5070 - val_acc: 0.7531


Run completed: runs/2024-05-06T19-41-07Z

Training run 24/52 (flags = list(64, 16, 0.01, 20, 50, "sigmoid", "sigmoid", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-41-34Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6391 - acc: 0.6085

254/254 [==============================] - 3s 7ms/step - loss: 0.6391 - acc: 0.6085 - val_loss: 0.5300 - val_acc: 0.7726
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5403 - acc: 0.7256

254/254 [==============================] - 1s 4ms/step - loss: 0.5403 - acc: 0.7256 - val_loss: 0.5017 - val_acc: 0.7655
Epoch 3/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5275 - acc: 0.7339 - val_loss: 0.4787 - val_acc: 0.7726
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5129 - acc: 0.7430

254/254 [==============================] - 1s 5ms/step - loss: 0.5129 - acc: 0.7430 - val_loss: 0.4859 - val_acc: 0.7655
Epoch 5/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5106 - acc: 0.7538 - val_loss: 0.4944 - val_acc: 0.7638
Epoch 6/50

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254/254 [==============================] - 1s 5ms/step - loss: 0.5087 - acc: 0.7495 - val_loss: 0.4877 - val_acc: 0.7638
Epoch 7/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5038 - acc: 0.7465 - val_loss: 0.4992 - val_acc: 0.7567
Epoch 8/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4975 - acc: 0.7497 - val_loss: 0.4944 - val_acc: 0.7584
Epoch 9/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4986 - acc: 0.7483 - val_loss: 0.4841 - val_acc: 0.7638
Epoch 10/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4943 - acc: 0.7504 - val_loss: 0.4760 - val_acc: 0.7620
Epoch 11/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4956 - acc: 0.7512 - val_loss: 0.4834 - val_acc: 0.7620
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4995 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4995 - acc: 0.7510 - val_loss: 0.4951 - val_acc: 0.7531
Epoch 13/50

  1/254 [..............................] - ETA: 1s - loss: 0.3495 - acc: 0.8000
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137/254 [===============>..............] - ETA: 0s - loss: 0.4988 - acc: 0.7478
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254/254 [==============================] - 1s 3ms/step - loss: 0.4942 - acc: 0.7491

254/254 [==============================] - 1s 4ms/step - loss: 0.4942 - acc: 0.7491 - val_loss: 0.4936 - val_acc: 0.7567
Epoch 14/50

  1/254 [..............................] - ETA: 0s - loss: 0.3737 - acc: 0.8500
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141/254 [===============>..............] - ETA: 0s - loss: 0.4880 - acc: 0.7500
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162/254 [==================>...........] - ETA: 0s - loss: 0.4877 - acc: 0.7515
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222/254 [=========================>....] - ETA: 0s - loss: 0.4902 - acc: 0.7493
240/254 [===========================>..] - ETA: 0s - loss: 0.4920 - acc: 0.7473
254/254 [==============================] - 1s 3ms/step - loss: 0.4912 - acc: 0.7485

254/254 [==============================] - 1s 5ms/step - loss: 0.4912 - acc: 0.7485 - val_loss: 0.4821 - val_acc: 0.7620
Epoch 15/50

  1/254 [..............................] - ETA: 0s - loss: 0.2798 - acc: 0.9000
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103/254 [===========>..................] - ETA: 0s - loss: 0.4688 - acc: 0.7655
117/254 [============>.................] - ETA: 0s - loss: 0.4729 - acc: 0.7585
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254/254 [==============================] - 1s 3ms/step - loss: 0.4883 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.4883 - acc: 0.7514 - val_loss: 0.4846 - val_acc: 0.7584
Epoch 16/50

  1/254 [..............................] - ETA: 1s - loss: 0.4819 - acc: 0.7500
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116/254 [============>.................] - ETA: 0s - loss: 0.4931 - acc: 0.7522
134/254 [==============>...............] - ETA: 0s - loss: 0.4966 - acc: 0.7489
152/254 [================>.............] - ETA: 0s - loss: 0.4958 - acc: 0.7487
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254/254 [==============================] - 1s 3ms/step - loss: 0.4920 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4920 - acc: 0.7510 - val_loss: 0.4869 - val_acc: 0.7620
Epoch 17/50

  1/254 [..............................] - ETA: 0s - loss: 0.3176 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4952 - acc: 0.7500

254/254 [==============================] - 1s 4ms/step - loss: 0.4952 - acc: 0.7500 - val_loss: 0.5001 - val_acc: 0.7496
Epoch 18/50

  1/254 [..............................] - ETA: 1s - loss: 0.3026 - acc: 0.8500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4882 - acc: 0.7532

254/254 [==============================] - 1s 4ms/step - loss: 0.4882 - acc: 0.7532 - val_loss: 0.4833 - val_acc: 0.7620
Epoch 19/50

  1/254 [..............................] - ETA: 0s - loss: 0.5162 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4900 - acc: 0.7579

254/254 [==============================] - 1s 4ms/step - loss: 0.4900 - acc: 0.7579 - val_loss: 0.4930 - val_acc: 0.7584
Epoch 20/50

  1/254 [..............................] - ETA: 0s - loss: 0.3454 - acc: 0.8000
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241/254 [===========================>..] - ETA: 0s - loss: 0.4993 - acc: 0.7517
254/254 [==============================] - 1s 3ms/step - loss: 0.4952 - acc: 0.7522

254/254 [==============================] - 1s 5ms/step - loss: 0.4952 - acc: 0.7522 - val_loss: 0.4795 - val_acc: 0.7620


Run completed: runs/2024-05-06T19-41-34Z

Training run 25/52 (flags = list(16, 64, 0.001, 50, 50, "tanh", "sigmoid", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-41-58Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:06 - loss: 0.7738 - acc: 0.6200

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0345s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0345s). Check your callbacks.


 18/102 [====>.........................] - ETA: 0s - loss: 0.7394 - acc: 0.6111  
 34/102 [=========>....................] - ETA: 0s - loss: 0.7268 - acc: 0.5865
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102/102 [==============================] - 2s 3ms/step - loss: 0.6823 - acc: 0.6030

102/102 [==============================] - 2s 11ms/step - loss: 0.6823 - acc: 0.6030 - val_loss: 0.5463 - val_acc: 0.7336
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.7102 - acc: 0.6400
 18/102 [====>.........................] - ETA: 0s - loss: 0.6182 - acc: 0.6600
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102/102 [==============================] - 0s 4ms/step - loss: 0.5766 - acc: 0.6972

102/102 [==============================] - 1s 6ms/step - loss: 0.5766 - acc: 0.6972 - val_loss: 0.4846 - val_acc: 0.7638
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6382 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5266 - acc: 0.7567
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 51/102 [==============>...............] - ETA: 0s - loss: 0.5304 - acc: 0.7400
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 85/102 [========================>.....] - ETA: 0s - loss: 0.5377 - acc: 0.7374
102/102 [==============================] - 0s 3ms/step - loss: 0.5425 - acc: 0.7359

102/102 [==============================] - 1s 7ms/step - loss: 0.5425 - acc: 0.7359 - val_loss: 0.4965 - val_acc: 0.7549
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.6872 - acc: 0.6600
 19/102 [====>.........................] - ETA: 0s - loss: 0.5572 - acc: 0.7074
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102/102 [==============================] - 0s 4ms/step - loss: 0.5403 - acc: 0.7361

102/102 [==============================] - 1s 6ms/step - loss: 0.5403 - acc: 0.7361 - val_loss: 0.4840 - val_acc: 0.7567
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.5059 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5282 - acc: 0.7511
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102/102 [==============================] - 0s 3ms/step - loss: 0.5237 - acc: 0.7447

102/102 [==============================] - 1s 5ms/step - loss: 0.5237 - acc: 0.7447 - val_loss: 0.4814 - val_acc: 0.7584
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.6424 - acc: 0.6600
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102/102 [==============================] - 0s 3ms/step - loss: 0.5253 - acc: 0.7459

102/102 [==============================] - 1s 6ms/step - loss: 0.5253 - acc: 0.7459 - val_loss: 0.4863 - val_acc: 0.7567
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.7094 - acc: 0.6200
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102/102 [==============================] - 0s 3ms/step - loss: 0.5233 - acc: 0.7424

102/102 [==============================] - 1s 6ms/step - loss: 0.5233 - acc: 0.7424 - val_loss: 0.4917 - val_acc: 0.7584
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.3890 - acc: 0.8600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5507 - acc: 0.7356
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102/102 [==============================] - 0s 3ms/step - loss: 0.5120 - acc: 0.7540

102/102 [==============================] - 1s 6ms/step - loss: 0.5120 - acc: 0.7540 - val_loss: 0.4850 - val_acc: 0.7584
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.5232 - acc: 0.6600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4876 - acc: 0.7588
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102/102 [==============================] - 0s 4ms/step - loss: 0.5098 - acc: 0.7457

102/102 [==============================] - 1s 8ms/step - loss: 0.5098 - acc: 0.7457 - val_loss: 0.4781 - val_acc: 0.7602
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4458 - acc: 0.7800
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102/102 [==============================] - 0s 4ms/step - loss: 0.5098 - acc: 0.7477

102/102 [==============================] - 1s 6ms/step - loss: 0.5098 - acc: 0.7477 - val_loss: 0.4809 - val_acc: 0.7584
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.4688 - acc: 0.8000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5096 - acc: 0.7433
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102/102 [==============================] - 0s 3ms/step - loss: 0.5109 - acc: 0.7502

102/102 [==============================] - 1s 5ms/step - loss: 0.5109 - acc: 0.7502 - val_loss: 0.4822 - val_acc: 0.7602
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.6181 - acc: 0.6600
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102/102 [==============================] - 0s 4ms/step - loss: 0.5048 - acc: 0.7489

102/102 [==============================] - 1s 7ms/step - loss: 0.5048 - acc: 0.7489 - val_loss: 0.4781 - val_acc: 0.7620
Epoch 13/50

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102/102 [==============================] - 0s 3ms/step - loss: 0.5018 - acc: 0.7569

102/102 [==============================] - 1s 5ms/step - loss: 0.5018 - acc: 0.7569 - val_loss: 0.4880 - val_acc: 0.7567
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.6563 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.5116 - acc: 0.7422

102/102 [==============================] - 1s 6ms/step - loss: 0.5116 - acc: 0.7422 - val_loss: 0.4854 - val_acc: 0.7567
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.4528 - acc: 0.6600
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102/102 [==============================] - 0s 4ms/step - loss: 0.5090 - acc: 0.7447

102/102 [==============================] - 1s 7ms/step - loss: 0.5090 - acc: 0.7447 - val_loss: 0.4805 - val_acc: 0.7549
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.5227 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.5087 - acc: 0.7514

102/102 [==============================] - 1s 5ms/step - loss: 0.5087 - acc: 0.7514 - val_loss: 0.4844 - val_acc: 0.7549
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.4127 - acc: 0.8600
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102/102 [==============================] - 0s 4ms/step - loss: 0.5064 - acc: 0.7437

102/102 [==============================] - 1s 6ms/step - loss: 0.5064 - acc: 0.7437 - val_loss: 0.4917 - val_acc: 0.7513
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.5210 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4926 - acc: 0.7622
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102/102 [==============================] - 0s 3ms/step - loss: 0.5033 - acc: 0.7546

102/102 [==============================] - 1s 6ms/step - loss: 0.5033 - acc: 0.7546 - val_loss: 0.4929 - val_acc: 0.7567
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.4305 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5139 - acc: 0.7329
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 87/102 [========================>.....] - ETA: 0s - loss: 0.5090 - acc: 0.7455
102/102 [==============================] - 0s 3ms/step - loss: 0.5058 - acc: 0.7465

102/102 [==============================] - 1s 5ms/step - loss: 0.5058 - acc: 0.7465 - val_loss: 0.4848 - val_acc: 0.7531


Run completed: runs/2024-05-06T19-41-58Z

Training run 26/52 (flags = list(32, 16, 0.01, 20, 30, "relu", "tanh", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-42-13Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:05 - loss: 0.6937 - acc: 0.5000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.


 20/254 [=>............................] - ETA: 0s - loss: 0.6886 - acc: 0.5825  
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240/254 [===========================>..] - ETA: 0s - loss: 0.6009 - acc: 0.6800
254/254 [==============================] - 2s 3ms/step - loss: 0.6011 - acc: 0.6811

254/254 [==============================] - 3s 7ms/step - loss: 0.6011 - acc: 0.6811 - val_loss: 0.5022 - val_acc: 0.7460
Epoch 2/30

  1/254 [..............................] - ETA: 1s - loss: 0.6600 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5524 - acc: 0.7238

254/254 [==============================] - 1s 4ms/step - loss: 0.5524 - acc: 0.7238 - val_loss: 0.4845 - val_acc: 0.7567
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5299 - acc: 0.7380

254/254 [==============================] - 1s 5ms/step - loss: 0.5299 - acc: 0.7380 - val_loss: 0.4780 - val_acc: 0.7691
Epoch 4/30

  1/254 [..............................] - ETA: 0s - loss: 0.4356 - acc: 0.6000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5282 - acc: 0.7311

254/254 [==============================] - 1s 4ms/step - loss: 0.5282 - acc: 0.7311 - val_loss: 0.4960 - val_acc: 0.7602
Epoch 5/30

  1/254 [..............................] - ETA: 0s - loss: 0.4234 - acc: 0.8500
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254/254 [==============================] - 1s 3ms/step - loss: 0.5173 - acc: 0.7339

254/254 [==============================] - 1s 4ms/step - loss: 0.5173 - acc: 0.7339 - val_loss: 0.4777 - val_acc: 0.7602
Epoch 6/30

  1/254 [..............................] - ETA: 0s - loss: 0.5533 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.5147 - acc: 0.7364

254/254 [==============================] - 1s 4ms/step - loss: 0.5147 - acc: 0.7364 - val_loss: 0.4722 - val_acc: 0.7655
Epoch 7/30

  1/254 [..............................] - ETA: 0s - loss: 0.4402 - acc: 0.9000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5089 - acc: 0.7355

254/254 [==============================] - 1s 4ms/step - loss: 0.5089 - acc: 0.7355 - val_loss: 0.4840 - val_acc: 0.7567
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5040 - acc: 0.7339

254/254 [==============================] - 1s 4ms/step - loss: 0.5040 - acc: 0.7339 - val_loss: 0.4982 - val_acc: 0.7478
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5051 - acc: 0.7347

254/254 [==============================] - 1s 4ms/step - loss: 0.5051 - acc: 0.7347 - val_loss: 0.5012 - val_acc: 0.7389
Epoch 10/30

  1/254 [..............................] - ETA: 0s - loss: 0.4215 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5025 - acc: 0.7323

254/254 [==============================] - 1s 4ms/step - loss: 0.5025 - acc: 0.7323 - val_loss: 0.4807 - val_acc: 0.7549
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4957 - acc: 0.7435

254/254 [==============================] - 1s 4ms/step - loss: 0.4957 - acc: 0.7435 - val_loss: 0.4687 - val_acc: 0.7567
Epoch 12/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4982 - acc: 0.7297

254/254 [==============================] - 1s 4ms/step - loss: 0.4982 - acc: 0.7297 - val_loss: 0.4751 - val_acc: 0.7584
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4992 - acc: 0.7437

254/254 [==============================] - 1s 4ms/step - loss: 0.4992 - acc: 0.7437 - val_loss: 0.4765 - val_acc: 0.7584
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4955 - acc: 0.7416

254/254 [==============================] - 1s 5ms/step - loss: 0.4955 - acc: 0.7416 - val_loss: 0.4677 - val_acc: 0.7620
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4926 - acc: 0.7437

254/254 [==============================] - 1s 4ms/step - loss: 0.4926 - acc: 0.7437 - val_loss: 0.4950 - val_acc: 0.7425
Epoch 16/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4925 - acc: 0.7341

254/254 [==============================] - 1s 4ms/step - loss: 0.4925 - acc: 0.7341 - val_loss: 0.4899 - val_acc: 0.7549
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4931 - acc: 0.7380

254/254 [==============================] - 1s 4ms/step - loss: 0.4931 - acc: 0.7380 - val_loss: 0.4641 - val_acc: 0.7620
Epoch 18/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4918 - acc: 0.7431

254/254 [==============================] - 1s 4ms/step - loss: 0.4918 - acc: 0.7431 - val_loss: 0.4812 - val_acc: 0.7460
Epoch 19/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4896 - acc: 0.7428

254/254 [==============================] - 1s 4ms/step - loss: 0.4896 - acc: 0.7428 - val_loss: 0.4851 - val_acc: 0.7549
Epoch 20/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4872 - acc: 0.7424

254/254 [==============================] - 1s 5ms/step - loss: 0.4872 - acc: 0.7424 - val_loss: 0.4858 - val_acc: 0.7460
Epoch 21/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4865 - acc: 0.7422

254/254 [==============================] - 1s 4ms/step - loss: 0.4865 - acc: 0.7422 - val_loss: 0.4852 - val_acc: 0.7496
Epoch 22/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4859 - acc: 0.7384

254/254 [==============================] - 1s 4ms/step - loss: 0.4859 - acc: 0.7384 - val_loss: 0.4767 - val_acc: 0.7567
Epoch 23/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4845 - acc: 0.7386

254/254 [==============================] - 1s 4ms/step - loss: 0.4845 - acc: 0.7386 - val_loss: 0.4792 - val_acc: 0.7567
Epoch 24/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4820 - acc: 0.7443

254/254 [==============================] - 1s 4ms/step - loss: 0.4820 - acc: 0.7443 - val_loss: 0.4930 - val_acc: 0.7460
Epoch 25/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4868 - acc: 0.7376

254/254 [==============================] - 1s 4ms/step - loss: 0.4868 - acc: 0.7376 - val_loss: 0.4783 - val_acc: 0.7602
Epoch 26/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4812 - acc: 0.7404

254/254 [==============================] - 1s 5ms/step - loss: 0.4812 - acc: 0.7404 - val_loss: 0.5004 - val_acc: 0.7407
Epoch 27/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4926 - acc: 0.7361

254/254 [==============================] - 1s 4ms/step - loss: 0.4926 - acc: 0.7361 - val_loss: 0.4951 - val_acc: 0.7425


Run completed: runs/2024-05-06T19-42-13Z

Training run 27/52 (flags = list(32, 32, 0.001, 20, 50, "sigmoid", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-42-43Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0298s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0298s). Check your callbacks.


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254/254 [==============================] - 2s 4ms/step - loss: 0.5883 - acc: 0.6777

254/254 [==============================] - 3s 7ms/step - loss: 0.5883 - acc: 0.6777 - val_loss: 0.4774 - val_acc: 0.7709
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5145 - acc: 0.7475

254/254 [==============================] - 1s 4ms/step - loss: 0.5145 - acc: 0.7475 - val_loss: 0.4948 - val_acc: 0.7584
Epoch 3/50

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254/254 [==============================] - 1s 5ms/step - loss: 0.5026 - acc: 0.7516 - val_loss: 0.4949 - val_acc: 0.7602
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5023 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.5023 - acc: 0.7514 - val_loss: 0.4702 - val_acc: 0.7691
Epoch 5/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4997 - acc: 0.7514 - val_loss: 0.4656 - val_acc: 0.7691
Epoch 6/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4961 - acc: 0.7542

254/254 [==============================] - 1s 5ms/step - loss: 0.4961 - acc: 0.7542 - val_loss: 0.4878 - val_acc: 0.7620
Epoch 7/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4952 - acc: 0.7534 - val_loss: 0.4993 - val_acc: 0.7549
Epoch 8/50

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254/254 [==============================] - 1s 5ms/step - loss: 0.4965 - acc: 0.7502 - val_loss: 0.4911 - val_acc: 0.7620
Epoch 9/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4913 - acc: 0.7538 - val_loss: 0.4737 - val_acc: 0.7655
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4885 - acc: 0.7571

254/254 [==============================] - 1s 4ms/step - loss: 0.4885 - acc: 0.7571 - val_loss: 0.5215 - val_acc: 0.7442
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4908 - acc: 0.7489

254/254 [==============================] - 1s 5ms/step - loss: 0.4908 - acc: 0.7489 - val_loss: 0.4890 - val_acc: 0.7602
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4856 - acc: 0.7526

254/254 [==============================] - 1s 4ms/step - loss: 0.4856 - acc: 0.7526 - val_loss: 0.5010 - val_acc: 0.7567
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4892 - acc: 0.7538

254/254 [==============================] - 1s 4ms/step - loss: 0.4892 - acc: 0.7538 - val_loss: 0.4816 - val_acc: 0.7602
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4889 - acc: 0.7459

254/254 [==============================] - 1s 5ms/step - loss: 0.4889 - acc: 0.7459 - val_loss: 0.4892 - val_acc: 0.7513
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4871 - acc: 0.7512

254/254 [==============================] - 1s 4ms/step - loss: 0.4871 - acc: 0.7512 - val_loss: 0.4827 - val_acc: 0.7602


Run completed: runs/2024-05-06T19-42-43Z

Training run 28/52 (flags = list(64, 32, 0.01, 20, 30, "tanh", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-43-02Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 7:45 - loss: 0.6229 - acc: 0.4000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1325s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1325s). Check your callbacks.


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254/254 [==============================] - 3s 4ms/step - loss: 0.5594 - acc: 0.7041

254/254 [==============================] - 4s 7ms/step - loss: 0.5594 - acc: 0.7041 - val_loss: 0.4852 - val_acc: 0.7673
Epoch 2/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5142 - acc: 0.7414

254/254 [==============================] - 1s 4ms/step - loss: 0.5142 - acc: 0.7414 - val_loss: 0.4790 - val_acc: 0.7744
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5060 - acc: 0.7548

254/254 [==============================] - 1s 5ms/step - loss: 0.5060 - acc: 0.7548 - val_loss: 0.5215 - val_acc: 0.7353
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4996 - acc: 0.7489

254/254 [==============================] - 1s 4ms/step - loss: 0.4996 - acc: 0.7489 - val_loss: 0.4810 - val_acc: 0.7638
Epoch 5/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4988 - acc: 0.7526 - val_loss: 0.4796 - val_acc: 0.7638
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4942 - acc: 0.7550

254/254 [==============================] - 1s 5ms/step - loss: 0.4942 - acc: 0.7550 - val_loss: 0.4835 - val_acc: 0.7620
Epoch 7/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4958 - acc: 0.7493 - val_loss: 0.4726 - val_acc: 0.7762
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4942 - acc: 0.7615

254/254 [==============================] - 1s 5ms/step - loss: 0.4942 - acc: 0.7615 - val_loss: 0.4824 - val_acc: 0.7726
Epoch 9/30

  1/254 [..............................] - ETA: 1s - loss: 0.5284 - acc: 0.8500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4960 - acc: 0.7573

254/254 [==============================] - 1s 4ms/step - loss: 0.4960 - acc: 0.7573 - val_loss: 0.4845 - val_acc: 0.7620
Epoch 10/30

  1/254 [..............................] - ETA: 1s - loss: 0.3993 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4932 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4932 - acc: 0.7550 - val_loss: 0.4845 - val_acc: 0.7655
Epoch 11/30

  1/254 [..............................] - ETA: 0s - loss: 0.4784 - acc: 0.6500
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241/254 [===========================>..] - ETA: 0s - loss: 0.4958 - acc: 0.7529
254/254 [==============================] - 1s 3ms/step - loss: 0.4924 - acc: 0.7544

254/254 [==============================] - 1s 5ms/step - loss: 0.4924 - acc: 0.7544 - val_loss: 0.4901 - val_acc: 0.7549
Epoch 12/30

  1/254 [..............................] - ETA: 1s - loss: 0.2926 - acc: 0.9500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4902 - acc: 0.7538

254/254 [==============================] - 1s 4ms/step - loss: 0.4902 - acc: 0.7538 - val_loss: 0.4997 - val_acc: 0.7460
Epoch 13/30

  1/254 [..............................] - ETA: 4s - loss: 0.5335 - acc: 0.6000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4835 - acc: 0.7579

254/254 [==============================] - 1s 4ms/step - loss: 0.4835 - acc: 0.7579 - val_loss: 0.4882 - val_acc: 0.7602
Epoch 14/30

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241/254 [===========================>..] - ETA: 0s - loss: 0.4896 - acc: 0.7548
254/254 [==============================] - 1s 4ms/step - loss: 0.4873 - acc: 0.7566

254/254 [==============================] - 1s 5ms/step - loss: 0.4873 - acc: 0.7566 - val_loss: 0.4888 - val_acc: 0.7620
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4897 - acc: 0.7599

254/254 [==============================] - 1s 4ms/step - loss: 0.4897 - acc: 0.7599 - val_loss: 0.5089 - val_acc: 0.7549
Epoch 16/30

  1/254 [..............................] - ETA: 1s - loss: 0.3536 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4817 - acc: 0.7589

254/254 [==============================] - 1s 4ms/step - loss: 0.4817 - acc: 0.7589 - val_loss: 0.5016 - val_acc: 0.7620
Epoch 17/30

  1/254 [..............................] - ETA: 0s - loss: 0.3893 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4907 - acc: 0.7611

254/254 [==============================] - 1s 4ms/step - loss: 0.4907 - acc: 0.7611 - val_loss: 0.4765 - val_acc: 0.7691


Run completed: runs/2024-05-06T19-43-02Z

Training run 29/52 (flags = list(32, 64, 0.001, 50, 50, "tanh", "relu", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-43-24Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:00 - loss: 0.7138 - acc: 0.5800

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0345s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0345s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.7199 - acc: 0.5437  
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102/102 [==============================] - 2s 3ms/step - loss: 0.6163 - acc: 0.6406

102/102 [==============================] - 2s 11ms/step - loss: 0.6163 - acc: 0.6406 - val_loss: 0.5309 - val_acc: 0.7300
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.5717 - acc: 0.6400
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102/102 [==============================] - 0s 4ms/step - loss: 0.5369 - acc: 0.7240

102/102 [==============================] - 1s 7ms/step - loss: 0.5369 - acc: 0.7240 - val_loss: 0.5057 - val_acc: 0.7549
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.3927 - acc: 0.9000
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102/102 [==============================] - 0s 3ms/step - loss: 0.5221 - acc: 0.7359

102/102 [==============================] - 1s 7ms/step - loss: 0.5221 - acc: 0.7359 - val_loss: 0.5217 - val_acc: 0.7407
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.4949 - acc: 0.7400
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102/102 [==============================] - 0s 3ms/step - loss: 0.5149 - acc: 0.7325

102/102 [==============================] - 1s 6ms/step - loss: 0.5149 - acc: 0.7325 - val_loss: 0.4915 - val_acc: 0.7496
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.5440 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.5063 - acc: 0.7459

102/102 [==============================] - 1s 7ms/step - loss: 0.5063 - acc: 0.7459 - val_loss: 0.5039 - val_acc: 0.7478
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.5269 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.5098 - acc: 0.7398

102/102 [==============================] - 1s 6ms/step - loss: 0.5098 - acc: 0.7398 - val_loss: 0.4854 - val_acc: 0.7584
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.5491 - acc: 0.8200
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102/102 [==============================] - 0s 4ms/step - loss: 0.4955 - acc: 0.7455

102/102 [==============================] - 1s 7ms/step - loss: 0.4955 - acc: 0.7455 - val_loss: 0.4872 - val_acc: 0.7549
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.5409 - acc: 0.7800
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 79/102 [======================>.......] - ETA: 0s - loss: 0.4982 - acc: 0.7514
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102/102 [==============================] - 0s 3ms/step - loss: 0.4983 - acc: 0.7479

102/102 [==============================] - 1s 8ms/step - loss: 0.4983 - acc: 0.7479 - val_loss: 0.4905 - val_acc: 0.7442
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.7071 - acc: 0.6200
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101/102 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.7428
102/102 [==============================] - 0s 3ms/step - loss: 0.5062 - acc: 0.7433

102/102 [==============================] - 1s 6ms/step - loss: 0.5062 - acc: 0.7433 - val_loss: 0.4936 - val_acc: 0.7496
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4108 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4965 - acc: 0.7418

102/102 [==============================] - 1s 7ms/step - loss: 0.4965 - acc: 0.7418 - val_loss: 0.4840 - val_acc: 0.7496
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.4420 - acc: 0.8000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4966 - acc: 0.7471

102/102 [==============================] - 1s 5ms/step - loss: 0.4966 - acc: 0.7471 - val_loss: 0.4860 - val_acc: 0.7567
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.6659 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.4928 - acc: 0.7500
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102/102 [==============================] - 0s 3ms/step - loss: 0.5015 - acc: 0.7506

102/102 [==============================] - 1s 7ms/step - loss: 0.5015 - acc: 0.7506 - val_loss: 0.4997 - val_acc: 0.7513
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.5984 - acc: 0.7200
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101/102 [============================>.] - ETA: 0s - loss: 0.4882 - acc: 0.7558
102/102 [==============================] - 0s 4ms/step - loss: 0.4878 - acc: 0.7554

102/102 [==============================] - 1s 7ms/step - loss: 0.4878 - acc: 0.7554 - val_loss: 0.4808 - val_acc: 0.7584
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.5090 - acc: 0.7000
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102/102 [==============================] - 0s 5ms/step - loss: 0.4990 - acc: 0.7579

102/102 [==============================] - 1s 8ms/step - loss: 0.4990 - acc: 0.7579 - val_loss: 0.4926 - val_acc: 0.7531
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.4864 - acc: 0.7800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4912 - acc: 0.7534

102/102 [==============================] - 1s 5ms/step - loss: 0.4912 - acc: 0.7534 - val_loss: 0.4863 - val_acc: 0.7602
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.4878 - acc: 0.7800
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102/102 [==============================] - 0s 3ms/step - loss: 0.5006 - acc: 0.7491

102/102 [==============================] - 1s 6ms/step - loss: 0.5006 - acc: 0.7491 - val_loss: 0.5039 - val_acc: 0.7442
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.5174 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.5000 - acc: 0.7477

102/102 [==============================] - 1s 7ms/step - loss: 0.5000 - acc: 0.7477 - val_loss: 0.4915 - val_acc: 0.7496
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.6213 - acc: 0.6400
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102/102 [==============================] - 0s 3ms/step - loss: 0.4979 - acc: 0.7471

102/102 [==============================] - 1s 7ms/step - loss: 0.4979 - acc: 0.7471 - val_loss: 0.4869 - val_acc: 0.7584
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.3691 - acc: 0.7800
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102/102 [==============================] - 0s 4ms/step - loss: 0.4931 - acc: 0.7613

102/102 [==============================] - 1s 6ms/step - loss: 0.4931 - acc: 0.7613 - val_loss: 0.4889 - val_acc: 0.7602
Epoch 20/50

  1/102 [..............................] - ETA: 0s - loss: 0.4180 - acc: 0.8400
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102/102 [==============================] - 0s 3ms/step - loss: 0.4998 - acc: 0.7554

102/102 [==============================] - 1s 6ms/step - loss: 0.4998 - acc: 0.7554 - val_loss: 0.4922 - val_acc: 0.7602
Epoch 21/50

  1/102 [..............................] - ETA: 1s - loss: 0.5264 - acc: 0.7000
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101/102 [============================>.] - ETA: 0s - loss: 0.4916 - acc: 0.7558
102/102 [==============================] - 0s 4ms/step - loss: 0.4917 - acc: 0.7558

102/102 [==============================] - 1s 8ms/step - loss: 0.4917 - acc: 0.7558 - val_loss: 0.4853 - val_acc: 0.7584
Epoch 22/50

  1/102 [..............................] - ETA: 0s - loss: 0.5571 - acc: 0.5400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4936 - acc: 0.7613

102/102 [==============================] - 1s 7ms/step - loss: 0.4936 - acc: 0.7613 - val_loss: 0.4948 - val_acc: 0.7584
Epoch 23/50

  1/102 [..............................] - ETA: 0s - loss: 0.5507 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4862 - acc: 0.7588
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100/102 [============================>.] - ETA: 0s - loss: 0.4929 - acc: 0.7520
102/102 [==============================] - 0s 3ms/step - loss: 0.4915 - acc: 0.7520

102/102 [==============================] - 1s 6ms/step - loss: 0.4915 - acc: 0.7520 - val_loss: 0.4927 - val_acc: 0.7567


Run completed: runs/2024-05-06T19-43-24Z

Training run 30/52 (flags = list(16, 32, 0.01, 20, 30, "relu", "relu", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-43-41Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:02 - loss: 0.6780 - acc: 0.7000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 17/254 [=>............................] - ETA: 0s - loss: 0.6600 - acc: 0.6206  
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244/254 [===========================>..] - ETA: 0s - loss: 0.6030 - acc: 0.6709
254/254 [==============================] - 2s 4ms/step - loss: 0.6018 - acc: 0.6726

254/254 [==============================] - 3s 7ms/step - loss: 0.6018 - acc: 0.6726 - val_loss: 0.5137 - val_acc: 0.7513
Epoch 2/30

  1/254 [..............................] - ETA: 0s - loss: 0.7072 - acc: 0.6000
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249/254 [============================>.] - ETA: 0s - loss: 0.5483 - acc: 0.7159
254/254 [==============================] - 1s 4ms/step - loss: 0.5483 - acc: 0.7173

254/254 [==============================] - 1s 4ms/step - loss: 0.5483 - acc: 0.7173 - val_loss: 0.4927 - val_acc: 0.7549
Epoch 3/30

  1/254 [..............................] - ETA: 1s - loss: 0.7141 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5331 - acc: 0.7280

254/254 [==============================] - 1s 4ms/step - loss: 0.5331 - acc: 0.7280 - val_loss: 0.5235 - val_acc: 0.7353
Epoch 4/30

  1/254 [..............................] - ETA: 0s - loss: 0.3294 - acc: 0.8000
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244/254 [===========================>..] - ETA: 0s - loss: 0.5286 - acc: 0.7186
254/254 [==============================] - 1s 4ms/step - loss: 0.5324 - acc: 0.7173

254/254 [==============================] - 1s 5ms/step - loss: 0.5324 - acc: 0.7173 - val_loss: 0.4784 - val_acc: 0.7709
Epoch 5/30

  1/254 [..............................] - ETA: 0s - loss: 0.3563 - acc: 0.9000
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253/254 [============================>.] - ETA: 0s - loss: 0.5284 - acc: 0.7277
254/254 [==============================] - 1s 3ms/step - loss: 0.5280 - acc: 0.7280

254/254 [==============================] - 1s 4ms/step - loss: 0.5280 - acc: 0.7280 - val_loss: 0.4949 - val_acc: 0.7460
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5175 - acc: 0.7309

254/254 [==============================] - 1s 5ms/step - loss: 0.5175 - acc: 0.7309 - val_loss: 0.4983 - val_acc: 0.7638
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5156 - acc: 0.7307

254/254 [==============================] - 1s 4ms/step - loss: 0.5156 - acc: 0.7307 - val_loss: 0.4850 - val_acc: 0.7691
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5193 - acc: 0.7370

254/254 [==============================] - 1s 4ms/step - loss: 0.5193 - acc: 0.7370 - val_loss: 0.4880 - val_acc: 0.7620
Epoch 9/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5073 - acc: 0.7364 - val_loss: 0.5017 - val_acc: 0.7584
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5091 - acc: 0.7366

254/254 [==============================] - 1s 4ms/step - loss: 0.5091 - acc: 0.7366 - val_loss: 0.5024 - val_acc: 0.7584
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5054 - acc: 0.7364

254/254 [==============================] - 1s 4ms/step - loss: 0.5054 - acc: 0.7364 - val_loss: 0.4902 - val_acc: 0.7638
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5112 - acc: 0.7366

254/254 [==============================] - 1s 4ms/step - loss: 0.5112 - acc: 0.7366 - val_loss: 0.4994 - val_acc: 0.7602
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5065 - acc: 0.7347

254/254 [==============================] - 1s 4ms/step - loss: 0.5065 - acc: 0.7347 - val_loss: 0.4952 - val_acc: 0.7549
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5067 - acc: 0.7329

254/254 [==============================] - 1s 5ms/step - loss: 0.5067 - acc: 0.7329 - val_loss: 0.4885 - val_acc: 0.7744


Run completed: runs/2024-05-06T19-43-41Z

Training run 31/52 (flags = list(64, 64, 0.001, 20, 30, "relu", "sigmoid", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-43-59Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5677 - acc: 0.6978

254/254 [==============================] - 3s 6ms/step - loss: 0.5677 - acc: 0.6978 - val_loss: 0.5053 - val_acc: 0.7620
Epoch 2/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5131 - acc: 0.7370

254/254 [==============================] - 1s 5ms/step - loss: 0.5131 - acc: 0.7370 - val_loss: 0.4791 - val_acc: 0.7726
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5004 - acc: 0.7481

254/254 [==============================] - 1s 4ms/step - loss: 0.5004 - acc: 0.7481 - val_loss: 0.4731 - val_acc: 0.7673
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4977 - acc: 0.7459

254/254 [==============================] - 1s 4ms/step - loss: 0.4977 - acc: 0.7459 - val_loss: 0.4987 - val_acc: 0.7513
Epoch 5/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4871 - acc: 0.7534

254/254 [==============================] - 1s 5ms/step - loss: 0.4871 - acc: 0.7534 - val_loss: 0.4718 - val_acc: 0.7655
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4873 - acc: 0.7538

254/254 [==============================] - 1s 4ms/step - loss: 0.4873 - acc: 0.7538 - val_loss: 0.4970 - val_acc: 0.7549
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4882 - acc: 0.7499

254/254 [==============================] - 1s 5ms/step - loss: 0.4882 - acc: 0.7499 - val_loss: 0.4916 - val_acc: 0.7496
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4831 - acc: 0.7552

254/254 [==============================] - 1s 4ms/step - loss: 0.4831 - acc: 0.7552 - val_loss: 0.4918 - val_acc: 0.7567
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4853 - acc: 0.7591

254/254 [==============================] - 1s 4ms/step - loss: 0.4853 - acc: 0.7591 - val_loss: 0.4682 - val_acc: 0.7638
Epoch 10/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4796 - acc: 0.7597

254/254 [==============================] - 1s 5ms/step - loss: 0.4796 - acc: 0.7597 - val_loss: 0.4719 - val_acc: 0.7638
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4796 - acc: 0.7648

254/254 [==============================] - 1s 4ms/step - loss: 0.4796 - acc: 0.7648 - val_loss: 0.4690 - val_acc: 0.7655
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4833 - acc: 0.7548

254/254 [==============================] - 1s 4ms/step - loss: 0.4833 - acc: 0.7548 - val_loss: 0.4862 - val_acc: 0.7496
Epoch 13/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4772 - acc: 0.7593

254/254 [==============================] - 1s 5ms/step - loss: 0.4772 - acc: 0.7593 - val_loss: 0.4860 - val_acc: 0.7655
Epoch 14/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4757 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4757 - acc: 0.7550 - val_loss: 0.4742 - val_acc: 0.7709
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4732 - acc: 0.7613

254/254 [==============================] - 1s 5ms/step - loss: 0.4732 - acc: 0.7613 - val_loss: 0.5033 - val_acc: 0.7478
Epoch 16/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4744 - acc: 0.7544

254/254 [==============================] - 1s 4ms/step - loss: 0.4744 - acc: 0.7544 - val_loss: 0.4698 - val_acc: 0.7744
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4712 - acc: 0.7656

254/254 [==============================] - 1s 4ms/step - loss: 0.4712 - acc: 0.7656 - val_loss: 0.5074 - val_acc: 0.7407
Epoch 18/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4701 - acc: 0.7581

254/254 [==============================] - 1s 5ms/step - loss: 0.4701 - acc: 0.7581 - val_loss: 0.4874 - val_acc: 0.7442
Epoch 19/30

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200/254 [======================>.......] - ETA: 0s - loss: 0.4707 - acc: 0.7632
216/254 [========================>.....] - ETA: 0s - loss: 0.4704 - acc: 0.7648
234/254 [==========================>...] - ETA: 0s - loss: 0.4751 - acc: 0.7603
250/254 [============================>.] - ETA: 0s - loss: 0.4715 - acc: 0.7632
254/254 [==============================] - 1s 3ms/step - loss: 0.4703 - acc: 0.7633

254/254 [==============================] - 1s 4ms/step - loss: 0.4703 - acc: 0.7633 - val_loss: 0.4696 - val_acc: 0.7815


Run completed: runs/2024-05-06T19-43-59Z

Training run 32/52 (flags = list(16, 16, 0.01, 50, 50, "sigmoid", "sigmoid", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-44-42Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:33 - loss: 0.7907 - acc: 0.2200

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0340s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0340s). Check your callbacks.


 17/102 [====>.........................] - ETA: 0s - loss: 0.7670 - acc: 0.2635  
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102/102 [==============================] - 2s 4ms/step - loss: 0.7110 - acc: 0.4278

102/102 [==============================] - 3s 13ms/step - loss: 0.7110 - acc: 0.4278 - val_loss: 0.6781 - val_acc: 0.5968
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.7821 - acc: 0.6600
  6/102 [>.............................] - ETA: 1s - loss: 0.6952 - acc: 0.5600
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 42/102 [===========>..................] - ETA: 0s - loss: 0.6757 - acc: 0.5343
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 71/102 [===================>..........] - ETA: 0s - loss: 0.6590 - acc: 0.5628
 84/102 [=======================>......] - ETA: 0s - loss: 0.6561 - acc: 0.5698
 95/102 [==========================>...] - ETA: 0s - loss: 0.6481 - acc: 0.5798
102/102 [==============================] - 0s 5ms/step - loss: 0.6440 - acc: 0.5843

102/102 [==============================] - 1s 7ms/step - loss: 0.6440 - acc: 0.5843 - val_loss: 0.6050 - val_acc: 0.7478
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6608 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5995 - acc: 0.6789
 34/102 [=========>....................] - ETA: 0s - loss: 0.6089 - acc: 0.6800
 42/102 [===========>..................] - ETA: 0s - loss: 0.6044 - acc: 0.6724
 59/102 [================>.............] - ETA: 0s - loss: 0.5986 - acc: 0.6790
 71/102 [===================>..........] - ETA: 0s - loss: 0.6015 - acc: 0.6828
 87/102 [========================>.....] - ETA: 0s - loss: 0.5974 - acc: 0.6848
102/102 [==============================] - 0s 4ms/step - loss: 0.5914 - acc: 0.6874

102/102 [==============================] - 1s 9ms/step - loss: 0.5914 - acc: 0.6874 - val_loss: 0.5595 - val_acc: 0.7531
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.5663 - acc: 0.7200
 15/102 [===>..........................] - ETA: 0s - loss: 0.5708 - acc: 0.7240
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 65/102 [==================>...........] - ETA: 0s - loss: 0.5639 - acc: 0.7194
 81/102 [======================>.......] - ETA: 0s - loss: 0.5563 - acc: 0.7212
100/102 [============================>.] - ETA: 0s - loss: 0.5518 - acc: 0.7226
102/102 [==============================] - 0s 3ms/step - loss: 0.5505 - acc: 0.7234

102/102 [==============================] - 1s 6ms/step - loss: 0.5505 - acc: 0.7234 - val_loss: 0.5103 - val_acc: 0.7673
Epoch 5/50

  1/102 [..............................] - ETA: 1s - loss: 0.6071 - acc: 0.7000
  7/102 [=>............................] - ETA: 0s - loss: 0.5825 - acc: 0.7286
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 87/102 [========================>.....] - ETA: 0s - loss: 0.5409 - acc: 0.7349
 97/102 [===========================>..] - ETA: 0s - loss: 0.5353 - acc: 0.7375
102/102 [==============================] - 0s 4ms/step - loss: 0.5357 - acc: 0.7347

102/102 [==============================] - 1s 8ms/step - loss: 0.5357 - acc: 0.7347 - val_loss: 0.5025 - val_acc: 0.7744
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.5203 - acc: 0.7600
 19/102 [====>.........................] - ETA: 0s - loss: 0.5193 - acc: 0.7684
 38/102 [==========>...................] - ETA: 0s - loss: 0.5089 - acc: 0.7637
 53/102 [==============>...............] - ETA: 0s - loss: 0.5099 - acc: 0.7623
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 81/102 [======================>.......] - ETA: 0s - loss: 0.5181 - acc: 0.7499
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102/102 [==============================] - 0s 4ms/step - loss: 0.5226 - acc: 0.7481

102/102 [==============================] - 1s 7ms/step - loss: 0.5226 - acc: 0.7481 - val_loss: 0.4992 - val_acc: 0.7726
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.5289 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5264 - acc: 0.7433
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 89/102 [=========================>....] - ETA: 0s - loss: 0.5213 - acc: 0.7443
102/102 [==============================] - 0s 3ms/step - loss: 0.5202 - acc: 0.7469

102/102 [==============================] - 1s 5ms/step - loss: 0.5202 - acc: 0.7469 - val_loss: 0.4946 - val_acc: 0.7655
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.4516 - acc: 0.8200
  5/102 [>.............................] - ETA: 1s - loss: 0.5499 - acc: 0.7520
 16/102 [===>..........................] - ETA: 0s - loss: 0.5587 - acc: 0.7262
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 90/102 [=========================>....] - ETA: 0s - loss: 0.5223 - acc: 0.7416
 98/102 [===========================>..] - ETA: 0s - loss: 0.5197 - acc: 0.7414
102/102 [==============================] - 0s 5ms/step - loss: 0.5171 - acc: 0.7428

102/102 [==============================] - 1s 8ms/step - loss: 0.5171 - acc: 0.7428 - val_loss: 0.4844 - val_acc: 0.7673
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.3921 - acc: 0.8000
 15/102 [===>..........................] - ETA: 0s - loss: 0.5210 - acc: 0.7427
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 99/102 [============================>.] - ETA: 0s - loss: 0.5080 - acc: 0.7475
102/102 [==============================] - 0s 5ms/step - loss: 0.5088 - acc: 0.7479

102/102 [==============================] - 1s 8ms/step - loss: 0.5088 - acc: 0.7479 - val_loss: 0.5011 - val_acc: 0.7638
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4595 - acc: 0.7200
 19/102 [====>.........................] - ETA: 0s - loss: 0.5134 - acc: 0.7484
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 57/102 [===============>..............] - ETA: 0s - loss: 0.5156 - acc: 0.7456
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 90/102 [=========================>....] - ETA: 0s - loss: 0.5152 - acc: 0.7416
102/102 [==============================] - 0s 3ms/step - loss: 0.5124 - acc: 0.7443

102/102 [==============================] - 1s 7ms/step - loss: 0.5124 - acc: 0.7443 - val_loss: 0.4802 - val_acc: 0.7709
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.4517 - acc: 0.7200
 19/102 [====>.........................] - ETA: 0s - loss: 0.5364 - acc: 0.7516
 38/102 [==========>...................] - ETA: 0s - loss: 0.5195 - acc: 0.7605
 55/102 [===============>..............] - ETA: 0s - loss: 0.5074 - acc: 0.7618
 73/102 [====================>.........] - ETA: 0s - loss: 0.5087 - acc: 0.7564
 90/102 [=========================>....] - ETA: 0s - loss: 0.5108 - acc: 0.7513
102/102 [==============================] - 0s 3ms/step - loss: 0.5110 - acc: 0.7495

102/102 [==============================] - 1s 6ms/step - loss: 0.5110 - acc: 0.7495 - val_loss: 0.4920 - val_acc: 0.7620
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.4760 - acc: 0.7600
 16/102 [===>..........................] - ETA: 0s - loss: 0.5204 - acc: 0.7538
 24/102 [======>.......................] - ETA: 0s - loss: 0.5207 - acc: 0.7500
 37/102 [=========>....................] - ETA: 0s - loss: 0.5161 - acc: 0.7481
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102/102 [==============================] - 0s 5ms/step - loss: 0.5047 - acc: 0.7487

102/102 [==============================] - 1s 8ms/step - loss: 0.5047 - acc: 0.7487 - val_loss: 0.4914 - val_acc: 0.7620
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.5047 - acc: 0.7800
 19/102 [====>.........................] - ETA: 0s - loss: 0.5022 - acc: 0.7432
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 88/102 [========================>.....] - ETA: 0s - loss: 0.5125 - acc: 0.7418
101/102 [============================>.] - ETA: 0s - loss: 0.5100 - acc: 0.7438
102/102 [==============================] - 0s 4ms/step - loss: 0.5095 - acc: 0.7437

102/102 [==============================] - 1s 8ms/step - loss: 0.5095 - acc: 0.7437 - val_loss: 0.4924 - val_acc: 0.7620
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.7086 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5231 - acc: 0.7541
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 86/102 [========================>.....] - ETA: 0s - loss: 0.5055 - acc: 0.7535
102/102 [==============================] - 0s 3ms/step - loss: 0.5024 - acc: 0.7560

102/102 [==============================] - 1s 6ms/step - loss: 0.5024 - acc: 0.7560 - val_loss: 0.4871 - val_acc: 0.7655
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.4378 - acc: 0.7600
 13/102 [==>...........................] - ETA: 0s - loss: 0.4925 - acc: 0.7554
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102/102 [==============================] - 0s 5ms/step - loss: 0.5007 - acc: 0.7571

102/102 [==============================] - 1s 8ms/step - loss: 0.5007 - acc: 0.7571 - val_loss: 0.4908 - val_acc: 0.7567
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.4790 - acc: 0.8000
 19/102 [====>.........................] - ETA: 0s - loss: 0.5183 - acc: 0.7505
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102/102 [==============================] - 0s 3ms/step - loss: 0.5013 - acc: 0.7485

102/102 [==============================] - 1s 6ms/step - loss: 0.5013 - acc: 0.7485 - val_loss: 0.4763 - val_acc: 0.7691
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.3972 - acc: 0.7600
 19/102 [====>.........................] - ETA: 0s - loss: 0.4917 - acc: 0.7474
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102/102 [==============================] - 0s 3ms/step - loss: 0.5010 - acc: 0.7544

102/102 [==============================] - 1s 7ms/step - loss: 0.5010 - acc: 0.7544 - val_loss: 0.4850 - val_acc: 0.7584
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.5688 - acc: 0.5800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4987 - acc: 0.7524

102/102 [==============================] - 1s 7ms/step - loss: 0.4987 - acc: 0.7524 - val_loss: 0.4870 - val_acc: 0.7549
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.4959 - acc: 0.7400
 19/102 [====>.........................] - ETA: 0s - loss: 0.5316 - acc: 0.7368
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 72/102 [====================>.........] - ETA: 0s - loss: 0.5057 - acc: 0.7525
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102/102 [==============================] - 0s 4ms/step - loss: 0.4970 - acc: 0.7528

102/102 [==============================] - 1s 6ms/step - loss: 0.4970 - acc: 0.7528 - val_loss: 0.4865 - val_acc: 0.7549
Epoch 20/50

  1/102 [..............................] - ETA: 0s - loss: 0.4900 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5023 - acc: 0.7478
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102/102 [==============================] - 0s 3ms/step - loss: 0.4979 - acc: 0.7536

102/102 [==============================] - 1s 5ms/step - loss: 0.4979 - acc: 0.7536 - val_loss: 0.4900 - val_acc: 0.7584
Epoch 21/50

  1/102 [..............................] - ETA: 0s - loss: 0.6024 - acc: 0.6800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5533 - acc: 0.7063
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101/102 [============================>.] - ETA: 0s - loss: 0.4982 - acc: 0.7477
102/102 [==============================] - 0s 4ms/step - loss: 0.4985 - acc: 0.7473

102/102 [==============================] - 1s 7ms/step - loss: 0.4985 - acc: 0.7473 - val_loss: 0.4844 - val_acc: 0.7549
Epoch 22/50

  1/102 [..............................] - ETA: 0s - loss: 0.4356 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4469 - acc: 0.7767
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102/102 [==============================] - 0s 3ms/step - loss: 0.4968 - acc: 0.7526

102/102 [==============================] - 1s 6ms/step - loss: 0.4968 - acc: 0.7526 - val_loss: 0.4863 - val_acc: 0.7549
Epoch 23/50

  1/102 [..............................] - ETA: 0s - loss: 0.3514 - acc: 0.8600
 15/102 [===>..........................] - ETA: 0s - loss: 0.4547 - acc: 0.7867
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 88/102 [========================>.....] - ETA: 0s - loss: 0.4868 - acc: 0.7598
102/102 [==============================] - 0s 3ms/step - loss: 0.4932 - acc: 0.7552

102/102 [==============================] - 1s 5ms/step - loss: 0.4932 - acc: 0.7552 - val_loss: 0.4943 - val_acc: 0.7567
Epoch 24/50

  1/102 [..............................] - ETA: 0s - loss: 0.7465 - acc: 0.6600
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102/102 [==============================] - 0s 4ms/step - loss: 0.4932 - acc: 0.7487

102/102 [==============================] - 1s 8ms/step - loss: 0.4932 - acc: 0.7487 - val_loss: 0.4889 - val_acc: 0.7567
Epoch 25/50

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102/102 [==============================] - 0s 3ms/step - loss: 0.4980 - acc: 0.7518

102/102 [==============================] - 1s 6ms/step - loss: 0.4980 - acc: 0.7518 - val_loss: 0.4905 - val_acc: 0.7567
Epoch 26/50

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102/102 [==============================] - 0s 3ms/step - loss: 0.4948 - acc: 0.7473

102/102 [==============================] - 1s 5ms/step - loss: 0.4948 - acc: 0.7473 - val_loss: 0.4881 - val_acc: 0.7584


Run completed: runs/2024-05-06T19-44-42Z

Training run 33/52 (flags = list(16, 32, 0.01, 20, 50, "relu", "relu", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-45-03Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:07 - loss: 0.8660 - acc: 0.7000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0287s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5817 - acc: 0.7041

254/254 [==============================] - 3s 6ms/step - loss: 0.5817 - acc: 0.7041 - val_loss: 0.5245 - val_acc: 0.7371
Epoch 2/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5124 - acc: 0.7410

254/254 [==============================] - 1s 5ms/step - loss: 0.5124 - acc: 0.7410 - val_loss: 0.5052 - val_acc: 0.7478
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5089 - acc: 0.7390

254/254 [==============================] - 1s 4ms/step - loss: 0.5089 - acc: 0.7390 - val_loss: 0.4946 - val_acc: 0.7496
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4972 - acc: 0.7473

254/254 [==============================] - 1s 4ms/step - loss: 0.4972 - acc: 0.7473 - val_loss: 0.5035 - val_acc: 0.7407
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4887 - acc: 0.7439

254/254 [==============================] - 1s 5ms/step - loss: 0.4887 - acc: 0.7439 - val_loss: 0.4927 - val_acc: 0.7673
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4940 - acc: 0.7491

254/254 [==============================] - 1s 4ms/step - loss: 0.4940 - acc: 0.7491 - val_loss: 0.4744 - val_acc: 0.7762
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4956 - acc: 0.7473

254/254 [==============================] - 1s 5ms/step - loss: 0.4956 - acc: 0.7473 - val_loss: 0.5025 - val_acc: 0.7549
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4827 - acc: 0.7457

254/254 [==============================] - 1s 4ms/step - loss: 0.4827 - acc: 0.7457 - val_loss: 0.4973 - val_acc: 0.7513
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4869 - acc: 0.7520

254/254 [==============================] - 1s 4ms/step - loss: 0.4869 - acc: 0.7520 - val_loss: 0.4812 - val_acc: 0.7709
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4886 - acc: 0.7506

254/254 [==============================] - 1s 5ms/step - loss: 0.4886 - acc: 0.7506 - val_loss: 0.4697 - val_acc: 0.7780
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4789 - acc: 0.7568

254/254 [==============================] - 1s 4ms/step - loss: 0.4789 - acc: 0.7568 - val_loss: 0.4997 - val_acc: 0.7549
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4794 - acc: 0.7575

254/254 [==============================] - 1s 5ms/step - loss: 0.4794 - acc: 0.7575 - val_loss: 0.4840 - val_acc: 0.7620
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4847 - acc: 0.7579

254/254 [==============================] - 1s 4ms/step - loss: 0.4847 - acc: 0.7579 - val_loss: 0.4852 - val_acc: 0.7567
Epoch 14/50

  1/254 [..............................] - ETA: 0s - loss: 0.6004 - acc: 0.6500
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116/254 [============>.................] - ETA: 0s - loss: 0.4852 - acc: 0.7457
132/254 [==============>...............] - ETA: 0s - loss: 0.4774 - acc: 0.7496
151/254 [================>.............] - ETA: 0s - loss: 0.4693 - acc: 0.7526
170/254 [===================>..........] - ETA: 0s - loss: 0.4717 - acc: 0.7579
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240/254 [===========================>..] - ETA: 0s - loss: 0.4751 - acc: 0.7596
254/254 [==============================] - 1s 3ms/step - loss: 0.4767 - acc: 0.7575

254/254 [==============================] - 1s 4ms/step - loss: 0.4767 - acc: 0.7575 - val_loss: 0.4834 - val_acc: 0.7638
Epoch 15/50

  1/254 [..............................] - ETA: 0s - loss: 0.3669 - acc: 0.9000
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108/254 [===========>..................] - ETA: 0s - loss: 0.4792 - acc: 0.7528
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132/254 [==============>...............] - ETA: 0s - loss: 0.4747 - acc: 0.7553
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240/254 [===========================>..] - ETA: 0s - loss: 0.4820 - acc: 0.7519
254/254 [==============================] - 1s 3ms/step - loss: 0.4810 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4810 - acc: 0.7524 - val_loss: 0.4921 - val_acc: 0.7602
Epoch 16/50

  1/254 [..............................] - ETA: 1s - loss: 0.5481 - acc: 0.7500
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111/254 [============>.................] - ETA: 0s - loss: 0.4844 - acc: 0.7509
129/254 [==============>...............] - ETA: 0s - loss: 0.4872 - acc: 0.7488
144/254 [================>.............] - ETA: 0s - loss: 0.4872 - acc: 0.7503
163/254 [==================>...........] - ETA: 0s - loss: 0.4846 - acc: 0.7479
181/254 [====================>.........] - ETA: 0s - loss: 0.4822 - acc: 0.7497
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254/254 [==============================] - 1s 3ms/step - loss: 0.4806 - acc: 0.7522

254/254 [==============================] - 1s 4ms/step - loss: 0.4806 - acc: 0.7522 - val_loss: 0.4867 - val_acc: 0.7567
Epoch 17/50

  1/254 [..............................] - ETA: 0s - loss: 0.5374 - acc: 0.7500
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146/254 [================>.............] - ETA: 0s - loss: 0.4731 - acc: 0.7507
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254/254 [==============================] - 1s 3ms/step - loss: 0.4738 - acc: 0.7583

254/254 [==============================] - 1s 4ms/step - loss: 0.4738 - acc: 0.7583 - val_loss: 0.4923 - val_acc: 0.7673
Epoch 18/50

  1/254 [..............................] - ETA: 1s - loss: 0.5657 - acc: 0.8000
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152/254 [================>.............] - ETA: 0s - loss: 0.4702 - acc: 0.7599
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254/254 [==============================] - 1s 3ms/step - loss: 0.4753 - acc: 0.7573

254/254 [==============================] - 1s 4ms/step - loss: 0.4753 - acc: 0.7573 - val_loss: 0.5028 - val_acc: 0.7531
Epoch 19/50

  1/254 [..............................] - ETA: 0s - loss: 0.3376 - acc: 0.8000
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238/254 [===========================>..] - ETA: 0s - loss: 0.4747 - acc: 0.7542
254/254 [==============================] - 1s 3ms/step - loss: 0.4741 - acc: 0.7518

254/254 [==============================] - 1s 4ms/step - loss: 0.4741 - acc: 0.7518 - val_loss: 0.4749 - val_acc: 0.7638
Epoch 20/50

  1/254 [..............................] - ETA: 0s - loss: 0.4244 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4723 - acc: 0.7514

254/254 [==============================] - 1s 5ms/step - loss: 0.4723 - acc: 0.7514 - val_loss: 0.4800 - val_acc: 0.7691


Run completed: runs/2024-05-06T19-45-03Z

Training run 34/52 (flags = list(64, 32, 0.001, 50, 30, "sigmoid", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-45-28Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:41 - loss: 0.7287 - acc: 0.3000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0300s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0300s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.7259 - acc: 0.4613  
 32/102 [========>.....................] - ETA: 0s - loss: 0.6983 - acc: 0.5394
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102/102 [==============================] - 2s 3ms/step - loss: 0.6035 - acc: 0.6560

102/102 [==============================] - 3s 12ms/step - loss: 0.6035 - acc: 0.6560 - val_loss: 0.5313 - val_acc: 0.7336
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.6126 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5040 - acc: 0.7494
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102/102 [==============================] - 0s 4ms/step - loss: 0.5194 - acc: 0.7398

102/102 [==============================] - 1s 6ms/step - loss: 0.5194 - acc: 0.7398 - val_loss: 0.4819 - val_acc: 0.7655
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.5202 - acc: 0.7600
 16/102 [===>..........................] - ETA: 0s - loss: 0.4928 - acc: 0.7825
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 97/102 [===========================>..] - ETA: 0s - loss: 0.5152 - acc: 0.7505
102/102 [==============================] - 0s 3ms/step - loss: 0.5145 - acc: 0.7506

102/102 [==============================] - 1s 6ms/step - loss: 0.5145 - acc: 0.7506 - val_loss: 0.4776 - val_acc: 0.7709
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.4445 - acc: 0.7600
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102/102 [==============================] - 1s 5ms/step - loss: 0.5112 - acc: 0.7520

102/102 [==============================] - 1s 8ms/step - loss: 0.5112 - acc: 0.7520 - val_loss: 0.4961 - val_acc: 0.7638
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.4604 - acc: 0.7800
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102/102 [==============================] - 0s 4ms/step - loss: 0.5079 - acc: 0.7487

102/102 [==============================] - 1s 7ms/step - loss: 0.5079 - acc: 0.7487 - val_loss: 0.4941 - val_acc: 0.7655
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.6328 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.5006 - acc: 0.7510

102/102 [==============================] - 1s 6ms/step - loss: 0.5006 - acc: 0.7510 - val_loss: 0.5155 - val_acc: 0.7531
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5197 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4759 - acc: 0.7435
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100/102 [============================>.] - ETA: 0s - loss: 0.5013 - acc: 0.7422
102/102 [==============================] - 0s 4ms/step - loss: 0.4999 - acc: 0.7437

102/102 [==============================] - 1s 7ms/step - loss: 0.4999 - acc: 0.7437 - val_loss: 0.4742 - val_acc: 0.7673
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.4391 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4518 - acc: 0.7922
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102/102 [==============================] - 0s 4ms/step - loss: 0.4958 - acc: 0.7583

102/102 [==============================] - 1s 6ms/step - loss: 0.4958 - acc: 0.7583 - val_loss: 0.4759 - val_acc: 0.7673
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4030 - acc: 0.8200
 16/102 [===>..........................] - ETA: 0s - loss: 0.4807 - acc: 0.7725
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 96/102 [===========================>..] - ETA: 0s - loss: 0.4903 - acc: 0.7535
102/102 [==============================] - 0s 4ms/step - loss: 0.4916 - acc: 0.7516

102/102 [==============================] - 1s 7ms/step - loss: 0.4916 - acc: 0.7516 - val_loss: 0.4980 - val_acc: 0.7567
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.3692 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4839 - acc: 0.7494
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102/102 [==============================] - 0s 4ms/step - loss: 0.4930 - acc: 0.7510

102/102 [==============================] - 1s 7ms/step - loss: 0.4930 - acc: 0.7510 - val_loss: 0.4866 - val_acc: 0.7620
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.6384 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4884 - acc: 0.7706
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 50/102 [=============>................] - ETA: 0s - loss: 0.4984 - acc: 0.7544
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102/102 [==============================] - 0s 4ms/step - loss: 0.4972 - acc: 0.7562

102/102 [==============================] - 1s 6ms/step - loss: 0.4972 - acc: 0.7562 - val_loss: 0.5252 - val_acc: 0.7353
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.4581 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4932 - acc: 0.7536

102/102 [==============================] - 1s 6ms/step - loss: 0.4932 - acc: 0.7536 - val_loss: 0.4667 - val_acc: 0.7744
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.6683 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5146 - acc: 0.7541
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102/102 [==============================] - 0s 4ms/step - loss: 0.4929 - acc: 0.7577

102/102 [==============================] - 1s 6ms/step - loss: 0.4929 - acc: 0.7577 - val_loss: 0.5255 - val_acc: 0.7425
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.4216 - acc: 0.8000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4931 - acc: 0.7569

102/102 [==============================] - 1s 8ms/step - loss: 0.4931 - acc: 0.7569 - val_loss: 0.4763 - val_acc: 0.7655
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.4819 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4597 - acc: 0.7553
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101/102 [============================>.] - ETA: 0s - loss: 0.4905 - acc: 0.7507
102/102 [==============================] - 0s 3ms/step - loss: 0.4900 - acc: 0.7510

102/102 [==============================] - 1s 5ms/step - loss: 0.4900 - acc: 0.7510 - val_loss: 0.4598 - val_acc: 0.7744
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.4220 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5031 - acc: 0.7600
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102/102 [==============================] - 0s 4ms/step - loss: 0.4890 - acc: 0.7546

102/102 [==============================] - 1s 6ms/step - loss: 0.4890 - acc: 0.7546 - val_loss: 0.4772 - val_acc: 0.7638
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.6088 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4932 - acc: 0.7306
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102/102 [==============================] - 0s 3ms/step - loss: 0.4861 - acc: 0.7546

102/102 [==============================] - 1s 6ms/step - loss: 0.4861 - acc: 0.7546 - val_loss: 0.4920 - val_acc: 0.7584
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4809 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.4912 - acc: 0.7435
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101/102 [============================>.] - ETA: 0s - loss: 0.4881 - acc: 0.7549
102/102 [==============================] - 0s 3ms/step - loss: 0.4874 - acc: 0.7552

102/102 [==============================] - 1s 6ms/step - loss: 0.4874 - acc: 0.7552 - val_loss: 0.4682 - val_acc: 0.7691
Epoch 19/30

  1/102 [..............................] - ETA: 0s - loss: 0.5388 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5235 - acc: 0.7541
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102/102 [==============================] - 0s 4ms/step - loss: 0.4858 - acc: 0.7526

102/102 [==============================] - 1s 6ms/step - loss: 0.4858 - acc: 0.7526 - val_loss: 0.4565 - val_acc: 0.7744
Epoch 20/30

  1/102 [..............................] - ETA: 0s - loss: 0.4706 - acc: 0.7600
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102/102 [==============================] - 0s 4ms/step - loss: 0.4854 - acc: 0.7585

102/102 [==============================] - 1s 7ms/step - loss: 0.4854 - acc: 0.7585 - val_loss: 0.4812 - val_acc: 0.7602
Epoch 21/30

  1/102 [..............................] - ETA: 0s - loss: 0.5092 - acc: 0.7200
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102/102 [==============================] - 0s 4ms/step - loss: 0.4849 - acc: 0.7583

102/102 [==============================] - 1s 6ms/step - loss: 0.4849 - acc: 0.7583 - val_loss: 0.4931 - val_acc: 0.7496
Epoch 22/30

  1/102 [..............................] - ETA: 0s - loss: 0.3164 - acc: 0.8600
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101/102 [============================>.] - ETA: 0s - loss: 0.4843 - acc: 0.7541
102/102 [==============================] - 0s 3ms/step - loss: 0.4847 - acc: 0.7536

102/102 [==============================] - 1s 6ms/step - loss: 0.4847 - acc: 0.7536 - val_loss: 0.4987 - val_acc: 0.7478
Epoch 23/30

  1/102 [..............................] - ETA: 0s - loss: 0.5255 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4876 - acc: 0.7536

102/102 [==============================] - 1s 5ms/step - loss: 0.4876 - acc: 0.7536 - val_loss: 0.4776 - val_acc: 0.7620
Epoch 24/30

  1/102 [..............................] - ETA: 0s - loss: 0.4673 - acc: 0.8000
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102/102 [==============================] - 0s 4ms/step - loss: 0.4866 - acc: 0.7544

102/102 [==============================] - 1s 6ms/step - loss: 0.4866 - acc: 0.7544 - val_loss: 0.4802 - val_acc: 0.7638
Epoch 25/30

  1/102 [..............................] - ETA: 0s - loss: 0.5123 - acc: 0.7400
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101/102 [============================>.] - ETA: 0s - loss: 0.4878 - acc: 0.7517
102/102 [==============================] - 0s 3ms/step - loss: 0.4871 - acc: 0.7522

102/102 [==============================] - 1s 6ms/step - loss: 0.4871 - acc: 0.7522 - val_loss: 0.4752 - val_acc: 0.7655
Epoch 26/30

  1/102 [..............................] - ETA: 0s - loss: 0.7165 - acc: 0.6000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4864 - acc: 0.7600
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102/102 [==============================] - 0s 4ms/step - loss: 0.4832 - acc: 0.7569

102/102 [==============================] - 1s 6ms/step - loss: 0.4832 - acc: 0.7569 - val_loss: 0.4871 - val_acc: 0.7531
Epoch 27/30

  1/102 [..............................] - ETA: 0s - loss: 0.6116 - acc: 0.6600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4836 - acc: 0.7566

102/102 [==============================] - 1s 6ms/step - loss: 0.4836 - acc: 0.7566 - val_loss: 0.5002 - val_acc: 0.7442
Epoch 28/30

  1/102 [..............................] - ETA: 0s - loss: 0.4109 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4811 - acc: 0.7341
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102/102 [==============================] - 0s 3ms/step - loss: 0.4856 - acc: 0.7530

102/102 [==============================] - 1s 5ms/step - loss: 0.4856 - acc: 0.7530 - val_loss: 0.5106 - val_acc: 0.7407
Epoch 29/30

  1/102 [..............................] - ETA: 0s - loss: 0.3324 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4997 - acc: 0.7350
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102/102 [==============================] - 0s 4ms/step - loss: 0.4838 - acc: 0.7532

102/102 [==============================] - 1s 6ms/step - loss: 0.4838 - acc: 0.7532 - val_loss: 0.4965 - val_acc: 0.7478


Run completed: runs/2024-05-06T19-45-28Z

Training run 35/52 (flags = list(32, 16, 0.001, 20, 50, "relu", "tanh", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-45-49Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:29 - loss: 0.6280 - acc: 0.8000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


 20/254 [=>............................] - ETA: 0s - loss: 0.8857 - acc: 0.4750  
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253/254 [============================>.] - ETA: 0s - loss: 0.6604 - acc: 0.6320
254/254 [==============================] - 2s 3ms/step - loss: 0.6605 - acc: 0.6320

254/254 [==============================] - 3s 6ms/step - loss: 0.6605 - acc: 0.6320 - val_loss: 0.4973 - val_acc: 0.7425
Epoch 2/50

  1/254 [..............................] - ETA: 1s - loss: 0.6808 - acc: 0.8500
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240/254 [===========================>..] - ETA: 0s - loss: 0.5601 - acc: 0.7042
254/254 [==============================] - 1s 3ms/step - loss: 0.5536 - acc: 0.7077

254/254 [==============================] - 1s 4ms/step - loss: 0.5536 - acc: 0.7077 - val_loss: 0.4837 - val_acc: 0.7496
Epoch 3/50

  1/254 [..............................] - ETA: 0s - loss: 0.4649 - acc: 0.8000
 19/254 [=>............................] - ETA: 0s - loss: 0.5686 - acc: 0.7395
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248/254 [============================>.] - ETA: 0s - loss: 0.5268 - acc: 0.7288
254/254 [==============================] - 1s 3ms/step - loss: 0.5268 - acc: 0.7294

254/254 [==============================] - 1s 5ms/step - loss: 0.5268 - acc: 0.7294 - val_loss: 0.4806 - val_acc: 0.7531
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 0.4397 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5173 - acc: 0.7250

254/254 [==============================] - 1s 4ms/step - loss: 0.5173 - acc: 0.7250 - val_loss: 0.4852 - val_acc: 0.7478
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5140 - acc: 0.7305

254/254 [==============================] - 1s 4ms/step - loss: 0.5140 - acc: 0.7305 - val_loss: 0.4771 - val_acc: 0.7602
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5067 - acc: 0.7276

254/254 [==============================] - 1s 4ms/step - loss: 0.5067 - acc: 0.7276 - val_loss: 0.4868 - val_acc: 0.7513
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5067 - acc: 0.7248

254/254 [==============================] - 1s 4ms/step - loss: 0.5067 - acc: 0.7248 - val_loss: 0.4702 - val_acc: 0.7602
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5101 - acc: 0.7272

254/254 [==============================] - 1s 5ms/step - loss: 0.5101 - acc: 0.7272 - val_loss: 0.4841 - val_acc: 0.7442
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5040 - acc: 0.7248

254/254 [==============================] - 1s 4ms/step - loss: 0.5040 - acc: 0.7248 - val_loss: 0.4774 - val_acc: 0.7460
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5000 - acc: 0.7262

254/254 [==============================] - 1s 4ms/step - loss: 0.5000 - acc: 0.7262 - val_loss: 0.4752 - val_acc: 0.7549
Epoch 11/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4973 - acc: 0.7333

254/254 [==============================] - 1s 5ms/step - loss: 0.4973 - acc: 0.7333 - val_loss: 0.4807 - val_acc: 0.7549
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4951 - acc: 0.7290

254/254 [==============================] - 1s 4ms/step - loss: 0.4951 - acc: 0.7290 - val_loss: 0.4817 - val_acc: 0.7531
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5042 - acc: 0.7335

254/254 [==============================] - 1s 4ms/step - loss: 0.5042 - acc: 0.7335 - val_loss: 0.4841 - val_acc: 0.7478
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4977 - acc: 0.7384

254/254 [==============================] - 1s 4ms/step - loss: 0.4977 - acc: 0.7384 - val_loss: 0.4851 - val_acc: 0.7442
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4928 - acc: 0.7307

254/254 [==============================] - 1s 4ms/step - loss: 0.4928 - acc: 0.7307 - val_loss: 0.4772 - val_acc: 0.7496
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5042 - acc: 0.7382

254/254 [==============================] - 1s 5ms/step - loss: 0.5042 - acc: 0.7382 - val_loss: 0.4813 - val_acc: 0.7442
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4914 - acc: 0.7309

254/254 [==============================] - 1s 4ms/step - loss: 0.4914 - acc: 0.7309 - val_loss: 0.4861 - val_acc: 0.7407


Run completed: runs/2024-05-06T19-45-49Z

Training run 36/52 (flags = list(32, 32, 0.001, 50, 30, "relu", "sigmoid", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-46-10Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:02 - loss: 0.7474 - acc: 0.4800

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


 17/102 [====>.........................] - ETA: 0s - loss: 0.7307 - acc: 0.5047  
 23/102 [=====>........................] - ETA: 0s - loss: 0.7190 - acc: 0.5148
 30/102 [=======>......................] - ETA: 0s - loss: 0.7025 - acc: 0.5420
 47/102 [============>.................] - ETA: 0s - loss: 0.6729 - acc: 0.5877
 59/102 [================>.............] - ETA: 0s - loss: 0.6667 - acc: 0.6047
 76/102 [=====================>........] - ETA: 0s - loss: 0.6494 - acc: 0.6274
 90/102 [=========================>....] - ETA: 0s - loss: 0.6321 - acc: 0.6402
102/102 [==============================] - 2s 4ms/step - loss: 0.6237 - acc: 0.6487

102/102 [==============================] - 2s 12ms/step - loss: 0.6237 - acc: 0.6487 - val_loss: 0.4978 - val_acc: 0.7638
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.5060 - acc: 0.6800
 15/102 [===>..........................] - ETA: 0s - loss: 0.5315 - acc: 0.7360
 32/102 [========>.....................] - ETA: 0s - loss: 0.5338 - acc: 0.7262
 44/102 [===========>..................] - ETA: 0s - loss: 0.5269 - acc: 0.7391
 58/102 [================>.............] - ETA: 0s - loss: 0.5336 - acc: 0.7372
 73/102 [====================>.........] - ETA: 0s - loss: 0.5302 - acc: 0.7364
 89/102 [=========================>....] - ETA: 0s - loss: 0.5287 - acc: 0.7398
 94/102 [==========================>...] - ETA: 0s - loss: 0.5256 - acc: 0.7417
102/102 [==============================] - 0s 4ms/step - loss: 0.5204 - acc: 0.7430

102/102 [==============================] - 1s 6ms/step - loss: 0.5204 - acc: 0.7430 - val_loss: 0.5010 - val_acc: 0.7549
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.5635 - acc: 0.7000
 12/102 [==>...........................] - ETA: 0s - loss: 0.5217 - acc: 0.7500
 14/102 [===>..........................] - ETA: 0s - loss: 0.5262 - acc: 0.7486
 24/102 [======>.......................] - ETA: 0s - loss: 0.5257 - acc: 0.7275
 42/102 [===========>..................] - ETA: 0s - loss: 0.5252 - acc: 0.7305
 60/102 [================>.............] - ETA: 0s - loss: 0.5179 - acc: 0.7423
 79/102 [======================>.......] - ETA: 0s - loss: 0.5134 - acc: 0.7504
 96/102 [===========================>..] - ETA: 0s - loss: 0.5092 - acc: 0.7467
102/102 [==============================] - 0s 4ms/step - loss: 0.5102 - acc: 0.7465

102/102 [==============================] - 1s 8ms/step - loss: 0.5102 - acc: 0.7465 - val_loss: 0.4773 - val_acc: 0.7673
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.2914 - acc: 0.8000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4937 - acc: 0.7700
 32/102 [========>.....................] - ETA: 0s - loss: 0.4886 - acc: 0.7663
 50/102 [=============>................] - ETA: 0s - loss: 0.4989 - acc: 0.7540
 68/102 [===================>..........] - ETA: 0s - loss: 0.5007 - acc: 0.7568
 85/102 [========================>.....] - ETA: 0s - loss: 0.4988 - acc: 0.7553
102/102 [==============================] - 0s 3ms/step - loss: 0.4986 - acc: 0.7566

102/102 [==============================] - 1s 6ms/step - loss: 0.4986 - acc: 0.7566 - val_loss: 0.4774 - val_acc: 0.7673
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.3936 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4864 - acc: 0.7500
 37/102 [=========>....................] - ETA: 0s - loss: 0.4884 - acc: 0.7541
 55/102 [===============>..............] - ETA: 0s - loss: 0.4951 - acc: 0.7553
 73/102 [====================>.........] - ETA: 0s - loss: 0.4991 - acc: 0.7515
 91/102 [=========================>....] - ETA: 0s - loss: 0.4962 - acc: 0.7514
102/102 [==============================] - 0s 3ms/step - loss: 0.4963 - acc: 0.7524

102/102 [==============================] - 1s 6ms/step - loss: 0.4963 - acc: 0.7524 - val_loss: 0.4888 - val_acc: 0.7602
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.4144 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5085 - acc: 0.7482
 34/102 [=========>....................] - ETA: 0s - loss: 0.5001 - acc: 0.7512
 51/102 [==============>...............] - ETA: 0s - loss: 0.5052 - acc: 0.7514
 63/102 [=================>............] - ETA: 0s - loss: 0.5011 - acc: 0.7552
 79/102 [======================>.......] - ETA: 0s - loss: 0.5065 - acc: 0.7478
 96/102 [===========================>..] - ETA: 0s - loss: 0.4984 - acc: 0.7515
102/102 [==============================] - 0s 3ms/step - loss: 0.4973 - acc: 0.7516

102/102 [==============================] - 1s 5ms/step - loss: 0.4973 - acc: 0.7516 - val_loss: 0.4690 - val_acc: 0.7744
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.3751 - acc: 0.8200
 19/102 [====>.........................] - ETA: 0s - loss: 0.4924 - acc: 0.7568
 38/102 [==========>...................] - ETA: 0s - loss: 0.4974 - acc: 0.7500
 56/102 [===============>..............] - ETA: 0s - loss: 0.5053 - acc: 0.7461
 72/102 [====================>.........] - ETA: 0s - loss: 0.5077 - acc: 0.7433
 90/102 [=========================>....] - ETA: 0s - loss: 0.4990 - acc: 0.7498
102/102 [==============================] - 0s 3ms/step - loss: 0.4945 - acc: 0.7514

102/102 [==============================] - 1s 5ms/step - loss: 0.4945 - acc: 0.7514 - val_loss: 0.4755 - val_acc: 0.7673
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5045 - acc: 0.7400
 12/102 [==>...........................] - ETA: 0s - loss: 0.5369 - acc: 0.7533
 29/102 [=======>......................] - ETA: 0s - loss: 0.5104 - acc: 0.7428
 38/102 [==========>...................] - ETA: 0s - loss: 0.5017 - acc: 0.7432
 46/102 [============>.................] - ETA: 0s - loss: 0.5001 - acc: 0.7422
 61/102 [================>.............] - ETA: 0s - loss: 0.4892 - acc: 0.7482
 75/102 [=====================>........] - ETA: 0s - loss: 0.4849 - acc: 0.7504
 93/102 [==========================>...] - ETA: 0s - loss: 0.4926 - acc: 0.7482
102/102 [==============================] - 0s 5ms/step - loss: 0.4922 - acc: 0.7495

102/102 [==============================] - 1s 8ms/step - loss: 0.4922 - acc: 0.7495 - val_loss: 0.5031 - val_acc: 0.7531
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.5026 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.4690 - acc: 0.7650
 33/102 [========>.....................] - ETA: 0s - loss: 0.4871 - acc: 0.7533
 50/102 [=============>................] - ETA: 0s - loss: 0.4900 - acc: 0.7488
 67/102 [==================>...........] - ETA: 0s - loss: 0.4831 - acc: 0.7564
 83/102 [=======================>......] - ETA: 0s - loss: 0.4900 - acc: 0.7561
100/102 [============================>.] - ETA: 0s - loss: 0.4940 - acc: 0.7520
102/102 [==============================] - 0s 3ms/step - loss: 0.4939 - acc: 0.7520

102/102 [==============================] - 1s 6ms/step - loss: 0.4939 - acc: 0.7520 - val_loss: 0.4948 - val_acc: 0.7478
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.4169 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4941 - acc: 0.7333
 35/102 [=========>....................] - ETA: 0s - loss: 0.4911 - acc: 0.7371
 53/102 [==============>...............] - ETA: 0s - loss: 0.4800 - acc: 0.7491
 72/102 [====================>.........] - ETA: 0s - loss: 0.4794 - acc: 0.7531
 78/102 [=====================>........] - ETA: 0s - loss: 0.4792 - acc: 0.7559
 95/102 [==========================>...] - ETA: 0s - loss: 0.4872 - acc: 0.7512
102/102 [==============================] - 0s 4ms/step - loss: 0.4865 - acc: 0.7497

102/102 [==============================] - 1s 6ms/step - loss: 0.4865 - acc: 0.7497 - val_loss: 0.4891 - val_acc: 0.7549
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5754 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4641 - acc: 0.7678
 37/102 [=========>....................] - ETA: 0s - loss: 0.4811 - acc: 0.7692
 54/102 [==============>...............] - ETA: 0s - loss: 0.4919 - acc: 0.7596
 71/102 [===================>..........] - ETA: 0s - loss: 0.4886 - acc: 0.7552
 87/102 [========================>.....] - ETA: 0s - loss: 0.4898 - acc: 0.7584
102/102 [==============================] - 0s 3ms/step - loss: 0.4889 - acc: 0.7552

102/102 [==============================] - 1s 5ms/step - loss: 0.4889 - acc: 0.7552 - val_loss: 0.4897 - val_acc: 0.7531
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.3978 - acc: 0.8000
 15/102 [===>..........................] - ETA: 0s - loss: 0.4862 - acc: 0.7587
 28/102 [=======>......................] - ETA: 0s - loss: 0.4919 - acc: 0.7521
 44/102 [===========>..................] - ETA: 0s - loss: 0.4908 - acc: 0.7518
 57/102 [===============>..............] - ETA: 0s - loss: 0.4848 - acc: 0.7582
 62/102 [=================>............] - ETA: 0s - loss: 0.4862 - acc: 0.7532
 76/102 [=====================>........] - ETA: 0s - loss: 0.4900 - acc: 0.7547
 93/102 [==========================>...] - ETA: 0s - loss: 0.4886 - acc: 0.7529
102/102 [==============================] - 0s 4ms/step - loss: 0.4864 - acc: 0.7538

102/102 [==============================] - 1s 7ms/step - loss: 0.4864 - acc: 0.7538 - val_loss: 0.4873 - val_acc: 0.7567
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.5667 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5255 - acc: 0.7289
 37/102 [=========>....................] - ETA: 0s - loss: 0.4925 - acc: 0.7411
 45/102 [============>.................] - ETA: 0s - loss: 0.4902 - acc: 0.7413
 51/102 [==============>...............] - ETA: 0s - loss: 0.4911 - acc: 0.7455
 70/102 [===================>..........] - ETA: 0s - loss: 0.4933 - acc: 0.7451
 82/102 [=======================>......] - ETA: 0s - loss: 0.4864 - acc: 0.7463
 99/102 [============================>.] - ETA: 0s - loss: 0.4905 - acc: 0.7463
102/102 [==============================] - 0s 4ms/step - loss: 0.4885 - acc: 0.7481

102/102 [==============================] - 1s 7ms/step - loss: 0.4885 - acc: 0.7481 - val_loss: 0.4778 - val_acc: 0.7638
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.3665 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4818 - acc: 0.7600
 33/102 [========>.....................] - ETA: 0s - loss: 0.4829 - acc: 0.7600
 47/102 [============>.................] - ETA: 0s - loss: 0.4788 - acc: 0.7617
 63/102 [=================>............] - ETA: 0s - loss: 0.4796 - acc: 0.7590
 79/102 [======================>.......] - ETA: 0s - loss: 0.4835 - acc: 0.7539
 94/102 [==========================>...] - ETA: 0s - loss: 0.4812 - acc: 0.7553
102/102 [==============================] - 0s 3ms/step - loss: 0.4793 - acc: 0.7579

102/102 [==============================] - 1s 6ms/step - loss: 0.4793 - acc: 0.7579 - val_loss: 0.4843 - val_acc: 0.7638
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5531 - acc: 0.6600
  5/102 [>.............................] - ETA: 1s - loss: 0.4881 - acc: 0.7440
 15/102 [===>..........................] - ETA: 0s - loss: 0.4776 - acc: 0.7453
 32/102 [========>.....................] - ETA: 0s - loss: 0.4841 - acc: 0.7406
 44/102 [===========>..................] - ETA: 0s - loss: 0.4868 - acc: 0.7395
 62/102 [=================>............] - ETA: 0s - loss: 0.4818 - acc: 0.7477
 77/102 [=====================>........] - ETA: 0s - loss: 0.4848 - acc: 0.7470
 92/102 [==========================>...] - ETA: 0s - loss: 0.4832 - acc: 0.7478
102/102 [==============================] - 0s 4ms/step - loss: 0.4834 - acc: 0.7504

102/102 [==============================] - 1s 7ms/step - loss: 0.4834 - acc: 0.7504 - val_loss: 0.4726 - val_acc: 0.7655
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.4749 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.4733 - acc: 0.7612
 35/102 [=========>....................] - ETA: 0s - loss: 0.4714 - acc: 0.7583
 53/102 [==============>...............] - ETA: 0s - loss: 0.4776 - acc: 0.7551
 70/102 [===================>..........] - ETA: 0s - loss: 0.4852 - acc: 0.7566
 87/102 [========================>.....] - ETA: 0s - loss: 0.4818 - acc: 0.7543
102/102 [==============================] - 0s 3ms/step - loss: 0.4849 - acc: 0.7546

102/102 [==============================] - 1s 6ms/step - loss: 0.4849 - acc: 0.7546 - val_loss: 0.4892 - val_acc: 0.7549


Run completed: runs/2024-05-06T19-46-10Z

Training run 37/52 (flags = list(16, 64, 0.001, 50, 50, "sigmoid", "tanh", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-46-23Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:05 - loss: 0.7254 - acc: 0.5400

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0357s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0357s). Check your callbacks.


 16/102 [===>..........................] - ETA: 0s - loss: 0.7089 - acc: 0.5325  
 33/102 [========>.....................] - ETA: 0s - loss: 0.6949 - acc: 0.5594
 49/102 [=============>................] - ETA: 0s - loss: 0.6930 - acc: 0.5596
 67/102 [==================>...........] - ETA: 0s - loss: 0.6782 - acc: 0.5743
 82/102 [=======================>......] - ETA: 0s - loss: 0.6699 - acc: 0.5863
101/102 [============================>.] - ETA: 0s - loss: 0.6583 - acc: 0.5990
102/102 [==============================] - 2s 4ms/step - loss: 0.6581 - acc: 0.5994

102/102 [==============================] - 2s 12ms/step - loss: 0.6581 - acc: 0.5994 - val_loss: 0.5405 - val_acc: 0.7691
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.5753 - acc: 0.7600
 14/102 [===>..........................] - ETA: 0s - loss: 0.6254 - acc: 0.6986
 32/102 [========>.....................] - ETA: 0s - loss: 0.5970 - acc: 0.6869
 47/102 [============>.................] - ETA: 0s - loss: 0.6025 - acc: 0.6791
 58/102 [================>.............] - ETA: 0s - loss: 0.5990 - acc: 0.6786
 74/102 [====================>.........] - ETA: 0s - loss: 0.5841 - acc: 0.6905
 88/102 [========================>.....] - ETA: 0s - loss: 0.5892 - acc: 0.6918
102/102 [==============================] - 0s 4ms/step - loss: 0.5817 - acc: 0.6951

102/102 [==============================] - 1s 6ms/step - loss: 0.5817 - acc: 0.6951 - val_loss: 0.5382 - val_acc: 0.7318
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6022 - acc: 0.7000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5547 - acc: 0.7138
 34/102 [=========>....................] - ETA: 0s - loss: 0.5697 - acc: 0.7100
 50/102 [=============>................] - ETA: 0s - loss: 0.5552 - acc: 0.7236
 65/102 [==================>...........] - ETA: 0s - loss: 0.5501 - acc: 0.7249
 81/102 [======================>.......] - ETA: 0s - loss: 0.5545 - acc: 0.7180
 99/102 [============================>.] - ETA: 0s - loss: 0.5631 - acc: 0.7075
102/102 [==============================] - 0s 3ms/step - loss: 0.5635 - acc: 0.7067

102/102 [==============================] - 1s 6ms/step - loss: 0.5635 - acc: 0.7067 - val_loss: 0.5023 - val_acc: 0.7602
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.4771 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5411 - acc: 0.7122
 36/102 [=========>....................] - ETA: 0s - loss: 0.5466 - acc: 0.7144
 50/102 [=============>................] - ETA: 0s - loss: 0.5454 - acc: 0.7164
 60/102 [================>.............] - ETA: 0s - loss: 0.5423 - acc: 0.7197
 77/102 [=====================>........] - ETA: 0s - loss: 0.5472 - acc: 0.7223
 91/102 [=========================>....] - ETA: 0s - loss: 0.5426 - acc: 0.7242
102/102 [==============================] - 0s 3ms/step - loss: 0.5452 - acc: 0.7209

102/102 [==============================] - 1s 6ms/step - loss: 0.5452 - acc: 0.7209 - val_loss: 0.5005 - val_acc: 0.7602
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.5095 - acc: 0.8400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5406 - acc: 0.7282
 34/102 [=========>....................] - ETA: 0s - loss: 0.5442 - acc: 0.7282
 51/102 [==============>...............] - ETA: 0s - loss: 0.5493 - acc: 0.7200
 68/102 [===================>..........] - ETA: 0s - loss: 0.5488 - acc: 0.7221
 84/102 [=======================>......] - ETA: 0s - loss: 0.5365 - acc: 0.7288
100/102 [============================>.] - ETA: 0s - loss: 0.5417 - acc: 0.7304
102/102 [==============================] - 0s 3ms/step - loss: 0.5409 - acc: 0.7309

102/102 [==============================] - 1s 5ms/step - loss: 0.5409 - acc: 0.7309 - val_loss: 0.5024 - val_acc: 0.7567
Epoch 6/50

  1/102 [..............................] - ETA: 1s - loss: 0.5922 - acc: 0.7200
 19/102 [====>.........................] - ETA: 0s - loss: 0.5224 - acc: 0.7253
 37/102 [=========>....................] - ETA: 0s - loss: 0.5210 - acc: 0.7259
 47/102 [============>.................] - ETA: 0s - loss: 0.5319 - acc: 0.7217
 49/102 [=============>................] - ETA: 0s - loss: 0.5380 - acc: 0.7208
 66/102 [==================>...........] - ETA: 0s - loss: 0.5297 - acc: 0.7264
 77/102 [=====================>........] - ETA: 0s - loss: 0.5323 - acc: 0.7223
 95/102 [==========================>...] - ETA: 0s - loss: 0.5258 - acc: 0.7284
101/102 [============================>.] - ETA: 0s - loss: 0.5269 - acc: 0.7285
102/102 [==============================] - 0s 4ms/step - loss: 0.5271 - acc: 0.7282

102/102 [==============================] - 1s 8ms/step - loss: 0.5271 - acc: 0.7282 - val_loss: 0.4830 - val_acc: 0.7602
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.4644 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5199 - acc: 0.7353
 35/102 [=========>....................] - ETA: 0s - loss: 0.5221 - acc: 0.7429
 52/102 [==============>...............] - ETA: 0s - loss: 0.5398 - acc: 0.7331
 64/102 [=================>............] - ETA: 0s - loss: 0.5327 - acc: 0.7300
 80/102 [======================>.......] - ETA: 0s - loss: 0.5329 - acc: 0.7303
 95/102 [==========================>...] - ETA: 0s - loss: 0.5337 - acc: 0.7284
102/102 [==============================] - 0s 3ms/step - loss: 0.5300 - acc: 0.7282

102/102 [==============================] - 1s 6ms/step - loss: 0.5300 - acc: 0.7282 - val_loss: 0.4864 - val_acc: 0.7602
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.4256 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5051 - acc: 0.7633
 36/102 [=========>....................] - ETA: 0s - loss: 0.5160 - acc: 0.7461
 54/102 [==============>...............] - ETA: 0s - loss: 0.5259 - acc: 0.7485
 71/102 [===================>..........] - ETA: 0s - loss: 0.5274 - acc: 0.7330
 88/102 [========================>.....] - ETA: 0s - loss: 0.5230 - acc: 0.7384
102/102 [==============================] - 0s 3ms/step - loss: 0.5212 - acc: 0.7392

102/102 [==============================] - 1s 6ms/step - loss: 0.5212 - acc: 0.7392 - val_loss: 0.4897 - val_acc: 0.7584
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.6815 - acc: 0.6800
  8/102 [=>............................] - ETA: 0s - loss: 0.5558 - acc: 0.7250
 21/102 [=====>........................] - ETA: 0s - loss: 0.5345 - acc: 0.7229
 39/102 [==========>...................] - ETA: 0s - loss: 0.5149 - acc: 0.7272
 52/102 [==============>...............] - ETA: 0s - loss: 0.5204 - acc: 0.7312
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100/102 [============================>.] - ETA: 0s - loss: 0.5192 - acc: 0.7362
102/102 [==============================] - 0s 4ms/step - loss: 0.5184 - acc: 0.7359

102/102 [==============================] - 1s 6ms/step - loss: 0.5184 - acc: 0.7359 - val_loss: 0.4807 - val_acc: 0.7655
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.6106 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5264 - acc: 0.7389
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102/102 [==============================] - 0s 3ms/step - loss: 0.5125 - acc: 0.7349

102/102 [==============================] - 1s 6ms/step - loss: 0.5125 - acc: 0.7349 - val_loss: 0.4818 - val_acc: 0.7620
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.6259 - acc: 0.7400
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102/102 [==============================] - 0s 3ms/step - loss: 0.5138 - acc: 0.7341

102/102 [==============================] - 1s 7ms/step - loss: 0.5138 - acc: 0.7341 - val_loss: 0.4792 - val_acc: 0.7638
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.5466 - acc: 0.7200
  6/102 [>.............................] - ETA: 1s - loss: 0.4844 - acc: 0.7900
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102/102 [==============================] - 0s 4ms/step - loss: 0.5123 - acc: 0.7390

102/102 [==============================] - 1s 7ms/step - loss: 0.5123 - acc: 0.7390 - val_loss: 0.4838 - val_acc: 0.7602
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.3642 - acc: 0.9000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4926 - acc: 0.7511
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102/102 [==============================] - 0s 3ms/step - loss: 0.5085 - acc: 0.7349

102/102 [==============================] - 1s 7ms/step - loss: 0.5085 - acc: 0.7349 - val_loss: 0.4716 - val_acc: 0.7655
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.4351 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5062 - acc: 0.7482
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 86/102 [========================>.....] - ETA: 0s - loss: 0.5120 - acc: 0.7393
102/102 [==============================] - 0s 3ms/step - loss: 0.5074 - acc: 0.7396

102/102 [==============================] - 1s 5ms/step - loss: 0.5074 - acc: 0.7396 - val_loss: 0.4826 - val_acc: 0.7584
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.3922 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5051 - acc: 0.7494
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102/102 [==============================] - 0s 3ms/step - loss: 0.5108 - acc: 0.7329

102/102 [==============================] - 1s 6ms/step - loss: 0.5108 - acc: 0.7329 - val_loss: 0.4838 - val_acc: 0.7584
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.5710 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4995 - acc: 0.7416

102/102 [==============================] - 1s 5ms/step - loss: 0.4995 - acc: 0.7416 - val_loss: 0.4838 - val_acc: 0.7584
Epoch 17/50

  1/102 [..............................] - ETA: 1s - loss: 0.6430 - acc: 0.6800
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102/102 [==============================] - 0s 4ms/step - loss: 0.5023 - acc: 0.7384

102/102 [==============================] - 1s 7ms/step - loss: 0.5023 - acc: 0.7384 - val_loss: 0.4847 - val_acc: 0.7584
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.5541 - acc: 0.7400
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102/102 [==============================] - 0s 3ms/step - loss: 0.5029 - acc: 0.7420

102/102 [==============================] - 1s 6ms/step - loss: 0.5029 - acc: 0.7420 - val_loss: 0.4840 - val_acc: 0.7567
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.3487 - acc: 0.8200
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102/102 [==============================] - 0s 4ms/step - loss: 0.5044 - acc: 0.7372

102/102 [==============================] - 1s 6ms/step - loss: 0.5044 - acc: 0.7372 - val_loss: 0.4964 - val_acc: 0.7531
Epoch 20/50

  1/102 [..............................] - ETA: 0s - loss: 0.5652 - acc: 0.7200
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102/102 [==============================] - 0s 3ms/step - loss: 0.5042 - acc: 0.7374

102/102 [==============================] - 1s 6ms/step - loss: 0.5042 - acc: 0.7374 - val_loss: 0.4812 - val_acc: 0.7638
Epoch 21/50

  1/102 [..............................] - ETA: 0s - loss: 0.5083 - acc: 0.6800
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102/102 [==============================] - 0s 3ms/step - loss: 0.4964 - acc: 0.7483

102/102 [==============================] - 1s 6ms/step - loss: 0.4964 - acc: 0.7483 - val_loss: 0.4745 - val_acc: 0.7691
Epoch 22/50

  1/102 [..............................] - ETA: 0s - loss: 0.4212 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5241 - acc: 0.7435
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102/102 [==============================] - 0s 3ms/step - loss: 0.4981 - acc: 0.7473

102/102 [==============================] - 1s 5ms/step - loss: 0.4981 - acc: 0.7473 - val_loss: 0.4898 - val_acc: 0.7584
Epoch 23/50

  1/102 [..............................] - ETA: 0s - loss: 0.3348 - acc: 0.8800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4831 - acc: 0.7422
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100/102 [============================>.] - ETA: 0s - loss: 0.4970 - acc: 0.7456
102/102 [==============================] - 0s 4ms/step - loss: 0.4977 - acc: 0.7441

102/102 [==============================] - 1s 7ms/step - loss: 0.4977 - acc: 0.7441 - val_loss: 0.5179 - val_acc: 0.7353


Run completed: runs/2024-05-06T19-46-23Z

Training run 38/52 (flags = list(32, 32, 0.001, 20, 50, "relu", "tanh", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-46-40Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:06 - loss: 0.6420 - acc: 0.5000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 18/254 [=>............................] - ETA: 0s - loss: 0.8003 - acc: 0.5194  
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239/254 [===========================>..] - ETA: 0s - loss: 0.6270 - acc: 0.6584
254/254 [==============================] - 2s 3ms/step - loss: 0.6205 - acc: 0.6619

254/254 [==============================] - 3s 7ms/step - loss: 0.6205 - acc: 0.6619 - val_loss: 0.4835 - val_acc: 0.7549
Epoch 2/50

  1/254 [..............................] - ETA: 2s - loss: 0.4789 - acc: 0.6500
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112/254 [============>.................] - ETA: 0s - loss: 0.5701 - acc: 0.6996
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254/254 [==============================] - 1s 4ms/step - loss: 0.5415 - acc: 0.7118

254/254 [==============================] - 1s 5ms/step - loss: 0.5415 - acc: 0.7118 - val_loss: 0.4706 - val_acc: 0.7602
Epoch 3/50

  1/254 [..............................] - ETA: 0s - loss: 0.9032 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.5254 - acc: 0.7284

254/254 [==============================] - 1s 4ms/step - loss: 0.5254 - acc: 0.7284 - val_loss: 0.4882 - val_acc: 0.7513
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 0.6111 - acc: 0.7000
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246/254 [============================>.] - ETA: 0s - loss: 0.5118 - acc: 0.7309
254/254 [==============================] - 1s 3ms/step - loss: 0.5150 - acc: 0.7307

254/254 [==============================] - 1s 4ms/step - loss: 0.5150 - acc: 0.7307 - val_loss: 0.4902 - val_acc: 0.7460
Epoch 5/50

  1/254 [..............................] - ETA: 1s - loss: 0.6446 - acc: 0.6500
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254/254 [==============================] - 1s 4ms/step - loss: 0.5059 - acc: 0.7341

254/254 [==============================] - 1s 4ms/step - loss: 0.5059 - acc: 0.7341 - val_loss: 0.4835 - val_acc: 0.7602
Epoch 6/50

  1/254 [..............................] - ETA: 1s - loss: 0.3470 - acc: 0.8000
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137/254 [===============>..............] - ETA: 0s - loss: 0.5017 - acc: 0.7372
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244/254 [===========================>..] - ETA: 0s - loss: 0.5041 - acc: 0.7291
254/254 [==============================] - 1s 3ms/step - loss: 0.5041 - acc: 0.7295

254/254 [==============================] - 1s 4ms/step - loss: 0.5041 - acc: 0.7295 - val_loss: 0.4929 - val_acc: 0.7549
Epoch 7/50

  1/254 [..............................] - ETA: 0s - loss: 0.4286 - acc: 0.7500
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144/254 [================>.............] - ETA: 0s - loss: 0.5046 - acc: 0.7351
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181/254 [====================>.........] - ETA: 0s - loss: 0.4973 - acc: 0.7420
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234/254 [==========================>...] - ETA: 0s - loss: 0.5003 - acc: 0.7361
253/254 [============================>.] - ETA: 0s - loss: 0.4992 - acc: 0.7352
254/254 [==============================] - 1s 3ms/step - loss: 0.4985 - acc: 0.7353

254/254 [==============================] - 1s 4ms/step - loss: 0.4985 - acc: 0.7353 - val_loss: 0.4747 - val_acc: 0.7620
Epoch 8/50

  1/254 [..............................] - ETA: 1s - loss: 0.5276 - acc: 0.7500
 15/254 [>.............................] - ETA: 1s - loss: 0.4417 - acc: 0.7833
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113/254 [============>.................] - ETA: 0s - loss: 0.4872 - acc: 0.7487
132/254 [==============>...............] - ETA: 0s - loss: 0.4893 - acc: 0.7511
151/254 [================>.............] - ETA: 0s - loss: 0.4963 - acc: 0.7457
169/254 [==================>...........] - ETA: 0s - loss: 0.4992 - acc: 0.7379
186/254 [====================>.........] - ETA: 0s - loss: 0.4950 - acc: 0.7398
204/254 [=======================>......] - ETA: 0s - loss: 0.4940 - acc: 0.7400
222/254 [=========================>....] - ETA: 0s - loss: 0.4956 - acc: 0.7383
240/254 [===========================>..] - ETA: 0s - loss: 0.4983 - acc: 0.7367
254/254 [==============================] - 1s 3ms/step - loss: 0.4987 - acc: 0.7353

254/254 [==============================] - 1s 5ms/step - loss: 0.4987 - acc: 0.7353 - val_loss: 0.5039 - val_acc: 0.7336
Epoch 9/50

  1/254 [..............................] - ETA: 0s - loss: 0.4266 - acc: 0.7000
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150/254 [================>.............] - ETA: 0s - loss: 0.4758 - acc: 0.7443
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188/254 [=====================>........] - ETA: 0s - loss: 0.4851 - acc: 0.7396
207/254 [=======================>......] - ETA: 0s - loss: 0.4866 - acc: 0.7394
225/254 [=========================>....] - ETA: 0s - loss: 0.4909 - acc: 0.7378
239/254 [===========================>..] - ETA: 0s - loss: 0.4918 - acc: 0.7358
254/254 [==============================] - 1s 3ms/step - loss: 0.4966 - acc: 0.7337

254/254 [==============================] - 1s 4ms/step - loss: 0.4966 - acc: 0.7337 - val_loss: 0.4942 - val_acc: 0.7389
Epoch 10/50

  1/254 [..............................] - ETA: 0s - loss: 0.4427 - acc: 0.8000
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240/254 [===========================>..] - ETA: 0s - loss: 0.4958 - acc: 0.7219
254/254 [==============================] - 1s 4ms/step - loss: 0.4917 - acc: 0.7262

254/254 [==============================] - 1s 4ms/step - loss: 0.4917 - acc: 0.7262 - val_loss: 0.4788 - val_acc: 0.7655
Epoch 11/50

  1/254 [..............................] - ETA: 0s - loss: 0.4706 - acc: 0.7000
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175/254 [===================>..........] - ETA: 0s - loss: 0.4866 - acc: 0.7509
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244/254 [===========================>..] - ETA: 0s - loss: 0.4870 - acc: 0.7461
254/254 [==============================] - 1s 3ms/step - loss: 0.4907 - acc: 0.7447

254/254 [==============================] - 1s 4ms/step - loss: 0.4907 - acc: 0.7447 - val_loss: 0.4902 - val_acc: 0.7478
Epoch 12/50

  1/254 [..............................] - ETA: 0s - loss: 0.2625 - acc: 0.7500
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239/254 [===========================>..] - ETA: 0s - loss: 0.4855 - acc: 0.7435
254/254 [==============================] - 1s 3ms/step - loss: 0.4908 - acc: 0.7424

254/254 [==============================] - 1s 4ms/step - loss: 0.4908 - acc: 0.7424 - val_loss: 0.4843 - val_acc: 0.7478


Run completed: runs/2024-05-06T19-46-40Z

Training run 39/52 (flags = list(32, 64, 0.001, 50, 50, "sigmoid", "relu", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-46-56Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:00 - loss: 0.7521 - acc: 0.4000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0323s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0323s). Check your callbacks.


 19/102 [====>.........................] - ETA: 0s - loss: 0.7219 - acc: 0.5474  
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102/102 [==============================] - 2s 3ms/step - loss: 0.6913 - acc: 0.5740

102/102 [==============================] - 2s 11ms/step - loss: 0.6913 - acc: 0.5740 - val_loss: 0.6205 - val_acc: 0.7105
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.6504 - acc: 0.5800
 14/102 [===>..........................] - ETA: 0s - loss: 0.6507 - acc: 0.6271
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100/102 [============================>.] - ETA: 0s - loss: 0.6113 - acc: 0.6696
102/102 [==============================] - 0s 4ms/step - loss: 0.6118 - acc: 0.6690

102/102 [==============================] - 1s 6ms/step - loss: 0.6118 - acc: 0.6690 - val_loss: 0.5305 - val_acc: 0.7549
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.6490 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5556 - acc: 0.7306
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102/102 [==============================] - 0s 3ms/step - loss: 0.5613 - acc: 0.7138

102/102 [==============================] - 1s 7ms/step - loss: 0.5613 - acc: 0.7138 - val_loss: 0.5023 - val_acc: 0.7673
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.5872 - acc: 0.6800
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 91/102 [=========================>....] - ETA: 0s - loss: 0.5472 - acc: 0.7376
102/102 [==============================] - 0s 4ms/step - loss: 0.5485 - acc: 0.7349

102/102 [==============================] - 1s 7ms/step - loss: 0.5485 - acc: 0.7349 - val_loss: 0.5082 - val_acc: 0.7638
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.4842 - acc: 0.7400
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101/102 [============================>.] - ETA: 0s - loss: 0.5336 - acc: 0.7311
102/102 [==============================] - 0s 4ms/step - loss: 0.5333 - acc: 0.7311

102/102 [==============================] - 1s 8ms/step - loss: 0.5333 - acc: 0.7311 - val_loss: 0.4883 - val_acc: 0.7744
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.4505 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5107 - acc: 0.7489
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102/102 [==============================] - 0s 3ms/step - loss: 0.5226 - acc: 0.7400

102/102 [==============================] - 1s 5ms/step - loss: 0.5226 - acc: 0.7400 - val_loss: 0.4879 - val_acc: 0.7655
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.3548 - acc: 0.8800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5405 - acc: 0.7541
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102/102 [==============================] - 0s 4ms/step - loss: 0.5177 - acc: 0.7384

102/102 [==============================] - 1s 7ms/step - loss: 0.5177 - acc: 0.7384 - val_loss: 0.4811 - val_acc: 0.7691
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.4553 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4998 - acc: 0.7541
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101/102 [============================>.] - ETA: 0s - loss: 0.5213 - acc: 0.7444
102/102 [==============================] - 0s 3ms/step - loss: 0.5210 - acc: 0.7447

102/102 [==============================] - 1s 6ms/step - loss: 0.5210 - acc: 0.7447 - val_loss: 0.4934 - val_acc: 0.7638
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.5224 - acc: 0.7000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5485 - acc: 0.7247
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102/102 [==============================] - 0s 3ms/step - loss: 0.5193 - acc: 0.7357

102/102 [==============================] - 1s 6ms/step - loss: 0.5193 - acc: 0.7357 - val_loss: 0.4774 - val_acc: 0.7691
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.5502 - acc: 0.6400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5190 - acc: 0.7412
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102/102 [==============================] - 1s 5ms/step - loss: 0.5129 - acc: 0.7394

102/102 [==============================] - 1s 8ms/step - loss: 0.5129 - acc: 0.7394 - val_loss: 0.4911 - val_acc: 0.7602
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.3905 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5024 - acc: 0.7353
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100/102 [============================>.] - ETA: 0s - loss: 0.5173 - acc: 0.7342
102/102 [==============================] - 0s 3ms/step - loss: 0.5180 - acc: 0.7345

102/102 [==============================] - 1s 7ms/step - loss: 0.5180 - acc: 0.7345 - val_loss: 0.4938 - val_acc: 0.7620
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.5684 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5095 - acc: 0.7411
 33/102 [========>.....................] - ETA: 0s - loss: 0.5020 - acc: 0.7491
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 81/102 [======================>.......] - ETA: 0s - loss: 0.5115 - acc: 0.7365
 99/102 [============================>.] - ETA: 0s - loss: 0.5082 - acc: 0.7430
102/102 [==============================] - 0s 3ms/step - loss: 0.5076 - acc: 0.7445

102/102 [==============================] - 1s 5ms/step - loss: 0.5076 - acc: 0.7445 - val_loss: 0.4762 - val_acc: 0.7691
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.3545 - acc: 0.7800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5074 - acc: 0.7525
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102/102 [==============================] - 0s 4ms/step - loss: 0.5068 - acc: 0.7443

102/102 [==============================] - 1s 6ms/step - loss: 0.5068 - acc: 0.7443 - val_loss: 0.4853 - val_acc: 0.7620
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.6281 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5271 - acc: 0.7282
 34/102 [=========>....................] - ETA: 0s - loss: 0.5092 - acc: 0.7353
 52/102 [==============>...............] - ETA: 0s - loss: 0.5155 - acc: 0.7350
 69/102 [===================>..........] - ETA: 0s - loss: 0.5129 - acc: 0.7336
 86/102 [========================>.....] - ETA: 0s - loss: 0.5053 - acc: 0.7363
102/102 [==============================] - 0s 3ms/step - loss: 0.5040 - acc: 0.7402

102/102 [==============================] - 1s 5ms/step - loss: 0.5040 - acc: 0.7402 - val_loss: 0.4809 - val_acc: 0.7655
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.5454 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.4852 - acc: 0.7563
 33/102 [========>.....................] - ETA: 0s - loss: 0.5025 - acc: 0.7479
 50/102 [=============>................] - ETA: 0s - loss: 0.5108 - acc: 0.7476
 66/102 [==================>...........] - ETA: 0s - loss: 0.5089 - acc: 0.7448
 81/102 [======================>.......] - ETA: 0s - loss: 0.5099 - acc: 0.7437
 99/102 [============================>.] - ETA: 0s - loss: 0.5068 - acc: 0.7467
102/102 [==============================] - 0s 3ms/step - loss: 0.5056 - acc: 0.7475

102/102 [==============================] - 1s 7ms/step - loss: 0.5056 - acc: 0.7475 - val_loss: 0.4856 - val_acc: 0.7655
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.4250 - acc: 0.7600
 18/102 [====>.........................] - ETA: 0s - loss: 0.4911 - acc: 0.7589
 35/102 [=========>....................] - ETA: 0s - loss: 0.4951 - acc: 0.7589
 43/102 [===========>..................] - ETA: 0s - loss: 0.4964 - acc: 0.7591
 49/102 [=============>................] - ETA: 0s - loss: 0.4984 - acc: 0.7539
 67/102 [==================>...........] - ETA: 0s - loss: 0.5061 - acc: 0.7493
 84/102 [=======================>......] - ETA: 0s - loss: 0.5042 - acc: 0.7507
 98/102 [===========================>..] - ETA: 0s - loss: 0.5002 - acc: 0.7527
102/102 [==============================] - 0s 4ms/step - loss: 0.5005 - acc: 0.7516

102/102 [==============================] - 1s 7ms/step - loss: 0.5005 - acc: 0.7516 - val_loss: 0.4796 - val_acc: 0.7673
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.4515 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5137 - acc: 0.7365
 34/102 [=========>....................] - ETA: 0s - loss: 0.5135 - acc: 0.7465
 49/102 [=============>................] - ETA: 0s - loss: 0.5234 - acc: 0.7400
 65/102 [==================>...........] - ETA: 0s - loss: 0.5183 - acc: 0.7428
 81/102 [======================>.......] - ETA: 0s - loss: 0.5078 - acc: 0.7477
 98/102 [===========================>..] - ETA: 0s - loss: 0.5048 - acc: 0.7457
102/102 [==============================] - 0s 3ms/step - loss: 0.5034 - acc: 0.7459

102/102 [==============================] - 1s 5ms/step - loss: 0.5034 - acc: 0.7459 - val_loss: 0.4826 - val_acc: 0.7673
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.3608 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.4954 - acc: 0.7694
 34/102 [=========>....................] - ETA: 0s - loss: 0.4952 - acc: 0.7582
 49/102 [=============>................] - ETA: 0s - loss: 0.4941 - acc: 0.7604
 66/102 [==================>...........] - ETA: 0s - loss: 0.4969 - acc: 0.7518
 80/102 [======================>.......] - ETA: 0s - loss: 0.4949 - acc: 0.7517
 96/102 [===========================>..] - ETA: 0s - loss: 0.4943 - acc: 0.7525
102/102 [==============================] - 0s 3ms/step - loss: 0.4957 - acc: 0.7500

102/102 [==============================] - 1s 6ms/step - loss: 0.4957 - acc: 0.7500 - val_loss: 0.4880 - val_acc: 0.7638
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.5279 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5248 - acc: 0.7341
 35/102 [=========>....................] - ETA: 0s - loss: 0.5152 - acc: 0.7389
 52/102 [==============>...............] - ETA: 0s - loss: 0.5221 - acc: 0.7362
 68/102 [===================>..........] - ETA: 0s - loss: 0.5112 - acc: 0.7415
 84/102 [=======================>......] - ETA: 0s - loss: 0.5037 - acc: 0.7500
 95/102 [==========================>...] - ETA: 0s - loss: 0.5037 - acc: 0.7491
102/102 [==============================] - 0s 4ms/step - loss: 0.5064 - acc: 0.7481

102/102 [==============================] - 1s 6ms/step - loss: 0.5064 - acc: 0.7481 - val_loss: 0.4848 - val_acc: 0.7691
Epoch 20/50

  1/102 [..............................] - ETA: 0s - loss: 0.6161 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5195 - acc: 0.7482
 34/102 [=========>....................] - ETA: 0s - loss: 0.5057 - acc: 0.7412
 51/102 [==============>...............] - ETA: 0s - loss: 0.5014 - acc: 0.7424
 67/102 [==================>...........] - ETA: 0s - loss: 0.4981 - acc: 0.7418
 83/102 [=======================>......] - ETA: 0s - loss: 0.4975 - acc: 0.7458
101/102 [============================>.] - ETA: 0s - loss: 0.4997 - acc: 0.7457
102/102 [==============================] - 0s 3ms/step - loss: 0.5007 - acc: 0.7459

102/102 [==============================] - 1s 5ms/step - loss: 0.5007 - acc: 0.7459 - val_loss: 0.4888 - val_acc: 0.7602
Epoch 21/50

  1/102 [..............................] - ETA: 0s - loss: 0.4598 - acc: 0.7400
  5/102 [>.............................] - ETA: 1s - loss: 0.4977 - acc: 0.7120
 23/102 [=====>........................] - ETA: 0s - loss: 0.4991 - acc: 0.7400
 32/102 [========>.....................] - ETA: 0s - loss: 0.5022 - acc: 0.7319
 49/102 [=============>................] - ETA: 0s - loss: 0.4957 - acc: 0.7420
 63/102 [=================>............] - ETA: 0s - loss: 0.4999 - acc: 0.7406
 73/102 [====================>.........] - ETA: 0s - loss: 0.4977 - acc: 0.7427
 81/102 [======================>.......] - ETA: 0s - loss: 0.5037 - acc: 0.7437
 98/102 [===========================>..] - ETA: 0s - loss: 0.5035 - acc: 0.7441
102/102 [==============================] - 0s 5ms/step - loss: 0.5025 - acc: 0.7455

102/102 [==============================] - 1s 8ms/step - loss: 0.5025 - acc: 0.7455 - val_loss: 0.4879 - val_acc: 0.7655
Epoch 22/50

  1/102 [..............................] - ETA: 0s - loss: 0.6606 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5006 - acc: 0.7388
 35/102 [=========>....................] - ETA: 0s - loss: 0.4992 - acc: 0.7417
 49/102 [=============>................] - ETA: 0s - loss: 0.4964 - acc: 0.7506
 65/102 [==================>...........] - ETA: 0s - loss: 0.4946 - acc: 0.7548
 80/102 [======================>.......] - ETA: 0s - loss: 0.4940 - acc: 0.7598
 89/102 [=========================>....] - ETA: 0s - loss: 0.4990 - acc: 0.7587
 95/102 [==========================>...] - ETA: 0s - loss: 0.4991 - acc: 0.7579
102/102 [==============================] - 0s 4ms/step - loss: 0.5000 - acc: 0.7568

102/102 [==============================] - 1s 7ms/step - loss: 0.5000 - acc: 0.7568 - val_loss: 0.4899 - val_acc: 0.7620


Run completed: runs/2024-05-06T19-46-56Z

Training run 40/52 (flags = list(32, 64, 0.001, 50, 30, "sigmoid", "tanh", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-47-12Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:47 - loss: 0.8287 - acc: 0.5000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0297s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0297s). Check your callbacks.


 18/102 [====>.........................] - ETA: 0s - loss: 0.7304 - acc: 0.5500  
 35/102 [=========>....................] - ETA: 0s - loss: 0.7391 - acc: 0.5651
 51/102 [==============>...............] - ETA: 0s - loss: 0.7286 - acc: 0.5596
 67/102 [==================>...........] - ETA: 0s - loss: 0.7230 - acc: 0.5609
 73/102 [====================>.........] - ETA: 0s - loss: 0.7143 - acc: 0.5658
 88/102 [========================>.....] - ETA: 0s - loss: 0.7054 - acc: 0.5757
102/102 [==============================] - 2s 4ms/step - loss: 0.7055 - acc: 0.5803

102/102 [==============================] - 3s 12ms/step - loss: 0.7055 - acc: 0.5803 - val_loss: 0.6199 - val_acc: 0.6732
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.6361 - acc: 0.6800
 14/102 [===>..........................] - ETA: 0s - loss: 0.6498 - acc: 0.5971
 22/102 [=====>........................] - ETA: 0s - loss: 0.6465 - acc: 0.6155
 31/102 [========>.....................] - ETA: 0s - loss: 0.6439 - acc: 0.6271
 48/102 [=============>................] - ETA: 0s - loss: 0.6277 - acc: 0.6375
 63/102 [=================>............] - ETA: 0s - loss: 0.6295 - acc: 0.6410
 75/102 [=====================>........] - ETA: 0s - loss: 0.6251 - acc: 0.6451
 92/102 [==========================>...] - ETA: 0s - loss: 0.6199 - acc: 0.6524
102/102 [==============================] - 0s 4ms/step - loss: 0.6172 - acc: 0.6529

102/102 [==============================] - 1s 6ms/step - loss: 0.6172 - acc: 0.6529 - val_loss: 0.5413 - val_acc: 0.7247
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.7264 - acc: 0.5800
 18/102 [====>.........................] - ETA: 0s - loss: 0.6012 - acc: 0.6878
 34/102 [=========>....................] - ETA: 0s - loss: 0.5875 - acc: 0.6900
 49/102 [=============>................] - ETA: 0s - loss: 0.5882 - acc: 0.6820
 66/102 [==================>...........] - ETA: 0s - loss: 0.5805 - acc: 0.6842
 83/102 [=======================>......] - ETA: 0s - loss: 0.5813 - acc: 0.6875
 98/102 [===========================>..] - ETA: 0s - loss: 0.5780 - acc: 0.6884
102/102 [==============================] - 0s 3ms/step - loss: 0.5757 - acc: 0.6903

102/102 [==============================] - 1s 5ms/step - loss: 0.5757 - acc: 0.6903 - val_loss: 0.4861 - val_acc: 0.7673
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.6426 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5467 - acc: 0.7113
 33/102 [========>.....................] - ETA: 0s - loss: 0.5585 - acc: 0.7200
 50/102 [=============>................] - ETA: 0s - loss: 0.5577 - acc: 0.7164
 65/102 [==================>...........] - ETA: 0s - loss: 0.5464 - acc: 0.7209
 81/102 [======================>.......] - ETA: 0s - loss: 0.5570 - acc: 0.7160
 96/102 [===========================>..] - ETA: 0s - loss: 0.5507 - acc: 0.7163
102/102 [==============================] - 0s 3ms/step - loss: 0.5544 - acc: 0.7175

102/102 [==============================] - 1s 6ms/step - loss: 0.5544 - acc: 0.7175 - val_loss: 0.4760 - val_acc: 0.7709
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.6567 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5444 - acc: 0.7247
 34/102 [=========>....................] - ETA: 0s - loss: 0.5231 - acc: 0.7376
 51/102 [==============>...............] - ETA: 0s - loss: 0.5215 - acc: 0.7420
 67/102 [==================>...........] - ETA: 0s - loss: 0.5206 - acc: 0.7376
 83/102 [=======================>......] - ETA: 0s - loss: 0.5300 - acc: 0.7316
101/102 [============================>.] - ETA: 0s - loss: 0.5297 - acc: 0.7350
102/102 [==============================] - 0s 3ms/step - loss: 0.5299 - acc: 0.7349

102/102 [==============================] - 1s 6ms/step - loss: 0.5299 - acc: 0.7349 - val_loss: 0.5057 - val_acc: 0.7567
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5400 - acc: 0.7200
  7/102 [=>............................] - ETA: 0s - loss: 0.5786 - acc: 0.7057
 24/102 [======>.......................] - ETA: 0s - loss: 0.5637 - acc: 0.7050
 41/102 [===========>..................] - ETA: 0s - loss: 0.5525 - acc: 0.7176
 49/102 [=============>................] - ETA: 0s - loss: 0.5507 - acc: 0.7163
 64/102 [=================>............] - ETA: 0s - loss: 0.5453 - acc: 0.7191
 80/102 [======================>.......] - ETA: 0s - loss: 0.5423 - acc: 0.7237
 94/102 [==========================>...] - ETA: 0s - loss: 0.5364 - acc: 0.7279
102/102 [==============================] - 0s 4ms/step - loss: 0.5349 - acc: 0.7282

102/102 [==============================] - 1s 7ms/step - loss: 0.5349 - acc: 0.7282 - val_loss: 0.4633 - val_acc: 0.7691
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5344 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5036 - acc: 0.7788
 35/102 [=========>....................] - ETA: 0s - loss: 0.5120 - acc: 0.7543
 52/102 [==============>...............] - ETA: 0s - loss: 0.5222 - acc: 0.7488
 68/102 [===================>..........] - ETA: 0s - loss: 0.5335 - acc: 0.7368
 82/102 [=======================>......] - ETA: 0s - loss: 0.5300 - acc: 0.7363
 89/102 [=========================>....] - ETA: 0s - loss: 0.5283 - acc: 0.7375
 99/102 [============================>.] - ETA: 0s - loss: 0.5253 - acc: 0.7386
102/102 [==============================] - 0s 4ms/step - loss: 0.5245 - acc: 0.7386

102/102 [==============================] - 1s 8ms/step - loss: 0.5245 - acc: 0.7386 - val_loss: 0.4865 - val_acc: 0.7638
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5730 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5532 - acc: 0.7482
 30/102 [=======>......................] - ETA: 0s - loss: 0.5336 - acc: 0.7420
 46/102 [============>.................] - ETA: 0s - loss: 0.5415 - acc: 0.7365
 62/102 [=================>............] - ETA: 0s - loss: 0.5308 - acc: 0.7368
 79/102 [======================>.......] - ETA: 0s - loss: 0.5289 - acc: 0.7377
 96/102 [===========================>..] - ETA: 0s - loss: 0.5266 - acc: 0.7354
102/102 [==============================] - 0s 3ms/step - loss: 0.5249 - acc: 0.7366

102/102 [==============================] - 1s 6ms/step - loss: 0.5249 - acc: 0.7366 - val_loss: 0.4745 - val_acc: 0.7655
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.5936 - acc: 0.7000
 12/102 [==>...........................] - ETA: 0s - loss: 0.5047 - acc: 0.7500
 29/102 [=======>......................] - ETA: 0s - loss: 0.5056 - acc: 0.7600
 42/102 [===========>..................] - ETA: 0s - loss: 0.5150 - acc: 0.7548
 50/102 [=============>................] - ETA: 0s - loss: 0.5152 - acc: 0.7504
 67/102 [==================>...........] - ETA: 0s - loss: 0.5129 - acc: 0.7484
 84/102 [=======================>......] - ETA: 0s - loss: 0.5101 - acc: 0.7448
 98/102 [===========================>..] - ETA: 0s - loss: 0.5104 - acc: 0.7469
102/102 [==============================] - 0s 4ms/step - loss: 0.5103 - acc: 0.7467

102/102 [==============================] - 1s 7ms/step - loss: 0.5103 - acc: 0.7467 - val_loss: 0.4865 - val_acc: 0.7638
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.5910 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.4833 - acc: 0.7424
 35/102 [=========>....................] - ETA: 0s - loss: 0.5015 - acc: 0.7497
 51/102 [==============>...............] - ETA: 0s - loss: 0.5148 - acc: 0.7357
 64/102 [=================>............] - ETA: 0s - loss: 0.5258 - acc: 0.7344
 77/102 [=====================>........] - ETA: 0s - loss: 0.5210 - acc: 0.7325
 87/102 [========================>.....] - ETA: 0s - loss: 0.5159 - acc: 0.7349
 99/102 [============================>.] - ETA: 0s - loss: 0.5137 - acc: 0.7386
102/102 [==============================] - 0s 4ms/step - loss: 0.5164 - acc: 0.7374

102/102 [==============================] - 1s 6ms/step - loss: 0.5164 - acc: 0.7374 - val_loss: 0.4761 - val_acc: 0.7655
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.5635 - acc: 0.6400
 17/102 [====>.........................] - ETA: 0s - loss: 0.4874 - acc: 0.7412
 34/102 [=========>....................] - ETA: 0s - loss: 0.5055 - acc: 0.7359
 51/102 [==============>...............] - ETA: 0s - loss: 0.5181 - acc: 0.7318
 67/102 [==================>...........] - ETA: 0s - loss: 0.5211 - acc: 0.7328
 82/102 [=======================>......] - ETA: 0s - loss: 0.5171 - acc: 0.7356
 99/102 [============================>.] - ETA: 0s - loss: 0.5092 - acc: 0.7400
102/102 [==============================] - 0s 3ms/step - loss: 0.5090 - acc: 0.7394

102/102 [==============================] - 1s 6ms/step - loss: 0.5090 - acc: 0.7394 - val_loss: 0.4837 - val_acc: 0.7638
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5220 - acc: 0.6400
 11/102 [==>...........................] - ETA: 0s - loss: 0.5021 - acc: 0.7436
 27/102 [======>.......................] - ETA: 0s - loss: 0.5136 - acc: 0.7378
 37/102 [=========>....................] - ETA: 0s - loss: 0.5132 - acc: 0.7384
 49/102 [=============>................] - ETA: 0s - loss: 0.4943 - acc: 0.7478
 65/102 [==================>...........] - ETA: 0s - loss: 0.4990 - acc: 0.7523
 82/102 [=======================>......] - ETA: 0s - loss: 0.5017 - acc: 0.7502
 96/102 [===========================>..] - ETA: 0s - loss: 0.5037 - acc: 0.7465
102/102 [==============================] - 0s 4ms/step - loss: 0.5024 - acc: 0.7461

102/102 [==============================] - 1s 8ms/step - loss: 0.5024 - acc: 0.7461 - val_loss: 0.4837 - val_acc: 0.7638
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.4849 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5287 - acc: 0.7365
 35/102 [=========>....................] - ETA: 0s - loss: 0.5105 - acc: 0.7423
 52/102 [==============>...............] - ETA: 0s - loss: 0.5077 - acc: 0.7465
 67/102 [==================>...........] - ETA: 0s - loss: 0.4965 - acc: 0.7564
 78/102 [=====================>........] - ETA: 0s - loss: 0.4988 - acc: 0.7521
 83/102 [=======================>......] - ETA: 0s - loss: 0.5015 - acc: 0.7494
 99/102 [============================>.] - ETA: 0s - loss: 0.5084 - acc: 0.7446
102/102 [==============================] - 0s 4ms/step - loss: 0.5070 - acc: 0.7457

102/102 [==============================] - 1s 6ms/step - loss: 0.5070 - acc: 0.7457 - val_loss: 0.4933 - val_acc: 0.7584
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.6118 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5161 - acc: 0.7341
 33/102 [========>.....................] - ETA: 0s - loss: 0.4938 - acc: 0.7364
 50/102 [=============>................] - ETA: 0s - loss: 0.5043 - acc: 0.7384
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 82/102 [=======================>......] - ETA: 0s - loss: 0.5029 - acc: 0.7388
 99/102 [============================>.] - ETA: 0s - loss: 0.5101 - acc: 0.7396
102/102 [==============================] - 0s 3ms/step - loss: 0.5091 - acc: 0.7390

102/102 [==============================] - 1s 5ms/step - loss: 0.5091 - acc: 0.7390 - val_loss: 0.5017 - val_acc: 0.7478
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.5418 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5213 - acc: 0.6944
 36/102 [=========>....................] - ETA: 0s - loss: 0.5083 - acc: 0.7239
 46/102 [============>.................] - ETA: 0s - loss: 0.5107 - acc: 0.7287
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 78/102 [=====================>........] - ETA: 0s - loss: 0.5082 - acc: 0.7310
 92/102 [==========================>...] - ETA: 0s - loss: 0.5078 - acc: 0.7335
102/102 [==============================] - 0s 3ms/step - loss: 0.5038 - acc: 0.7372

102/102 [==============================] - 1s 6ms/step - loss: 0.5038 - acc: 0.7372 - val_loss: 0.4852 - val_acc: 0.7638
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.4503 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5134 - acc: 0.7647
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102/102 [==============================] - 0s 3ms/step - loss: 0.5045 - acc: 0.7416

102/102 [==============================] - 1s 6ms/step - loss: 0.5045 - acc: 0.7416 - val_loss: 0.4708 - val_acc: 0.7673


Run completed: runs/2024-05-06T19-47-12Z

Training run 41/52 (flags = list(16, 16, 0.001, 20, 50, "sigmoid", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-47-26Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0332s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0332s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6702 - acc: 0.6377

254/254 [==============================] - 3s 6ms/step - loss: 0.6702 - acc: 0.6377 - val_loss: 0.5529 - val_acc: 0.7460
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5783 - acc: 0.6899

254/254 [==============================] - 1s 4ms/step - loss: 0.5783 - acc: 0.6899 - val_loss: 0.5198 - val_acc: 0.7478
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5514 - acc: 0.7258

254/254 [==============================] - 1s 5ms/step - loss: 0.5514 - acc: 0.7258 - val_loss: 0.4982 - val_acc: 0.7584
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5294 - acc: 0.7254

254/254 [==============================] - 1s 4ms/step - loss: 0.5294 - acc: 0.7254 - val_loss: 0.4855 - val_acc: 0.7549
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5303 - acc: 0.7351

254/254 [==============================] - 1s 4ms/step - loss: 0.5303 - acc: 0.7351 - val_loss: 0.4743 - val_acc: 0.7655
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5292 - acc: 0.7390

254/254 [==============================] - 1s 5ms/step - loss: 0.5292 - acc: 0.7390 - val_loss: 0.4799 - val_acc: 0.7567
Epoch 7/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5242 - acc: 0.7305

254/254 [==============================] - 1s 4ms/step - loss: 0.5242 - acc: 0.7305 - val_loss: 0.4900 - val_acc: 0.7513
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5203 - acc: 0.7339

254/254 [==============================] - 1s 4ms/step - loss: 0.5203 - acc: 0.7339 - val_loss: 0.4762 - val_acc: 0.7655
Epoch 9/50

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254/254 [==============================] - 1s 5ms/step - loss: 0.5057 - acc: 0.7431 - val_loss: 0.4866 - val_acc: 0.7549
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5089 - acc: 0.7370

254/254 [==============================] - 1s 4ms/step - loss: 0.5089 - acc: 0.7370 - val_loss: 0.4705 - val_acc: 0.7638
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5193 - acc: 0.7323

254/254 [==============================] - 1s 4ms/step - loss: 0.5193 - acc: 0.7323 - val_loss: 0.4731 - val_acc: 0.7673
Epoch 12/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5141 - acc: 0.7325 - val_loss: 0.4861 - val_acc: 0.7478
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5070 - acc: 0.7319

254/254 [==============================] - 1s 4ms/step - loss: 0.5070 - acc: 0.7319 - val_loss: 0.4821 - val_acc: 0.7513
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5067 - acc: 0.7333

254/254 [==============================] - 1s 4ms/step - loss: 0.5067 - acc: 0.7333 - val_loss: 0.4736 - val_acc: 0.7549
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5148 - acc: 0.7325

254/254 [==============================] - 1s 4ms/step - loss: 0.5148 - acc: 0.7325 - val_loss: 0.4946 - val_acc: 0.7442
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5154 - acc: 0.7311

254/254 [==============================] - 1s 4ms/step - loss: 0.5154 - acc: 0.7311 - val_loss: 0.4978 - val_acc: 0.7389
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5007 - acc: 0.7408

254/254 [==============================] - 1s 4ms/step - loss: 0.5007 - acc: 0.7408 - val_loss: 0.4792 - val_acc: 0.7638
Epoch 18/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5076 - acc: 0.7400

254/254 [==============================] - 1s 5ms/step - loss: 0.5076 - acc: 0.7400 - val_loss: 0.4904 - val_acc: 0.7442
Epoch 19/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5021 - acc: 0.7329

254/254 [==============================] - 1s 4ms/step - loss: 0.5021 - acc: 0.7329 - val_loss: 0.4870 - val_acc: 0.7478
Epoch 20/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5045 - acc: 0.7414

254/254 [==============================] - 1s 4ms/step - loss: 0.5045 - acc: 0.7414 - val_loss: 0.5045 - val_acc: 0.7300


Run completed: runs/2024-05-06T19-47-26Z

Training run 42/52 (flags = list(32, 64, 0.01, 20, 30, "relu", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-47-50Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:07 - loss: 0.7397 - acc: 0.4000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5761 - acc: 0.6826

254/254 [==============================] - 3s 6ms/step - loss: 0.5761 - acc: 0.6826 - val_loss: 0.5114 - val_acc: 0.7549
Epoch 2/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5219 - acc: 0.7398

254/254 [==============================] - 1s 5ms/step - loss: 0.5219 - acc: 0.7398 - val_loss: 0.4896 - val_acc: 0.7602
Epoch 3/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4993 - acc: 0.7471

254/254 [==============================] - 1s 4ms/step - loss: 0.4993 - acc: 0.7471 - val_loss: 0.4733 - val_acc: 0.7620
Epoch 4/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4953 - acc: 0.7506

254/254 [==============================] - 1s 4ms/step - loss: 0.4953 - acc: 0.7506 - val_loss: 0.4957 - val_acc: 0.7460
Epoch 5/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4953 - acc: 0.7483

254/254 [==============================] - 1s 5ms/step - loss: 0.4953 - acc: 0.7483 - val_loss: 0.4882 - val_acc: 0.7513
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4858 - acc: 0.7550

254/254 [==============================] - 1s 4ms/step - loss: 0.4858 - acc: 0.7550 - val_loss: 0.4855 - val_acc: 0.7460
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4900 - acc: 0.7556

254/254 [==============================] - 1s 4ms/step - loss: 0.4900 - acc: 0.7556 - val_loss: 0.4841 - val_acc: 0.7513
Epoch 8/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4844 - acc: 0.7552 - val_loss: 0.4685 - val_acc: 0.7638
Epoch 9/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4823 - acc: 0.7597 - val_loss: 0.4728 - val_acc: 0.7691
Epoch 10/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4831 - acc: 0.7577 - val_loss: 0.4967 - val_acc: 0.7389
Epoch 11/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4808 - acc: 0.7601 - val_loss: 0.4850 - val_acc: 0.7513
Epoch 12/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4802 - acc: 0.7629 - val_loss: 0.4878 - val_acc: 0.7513
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4765 - acc: 0.7577

254/254 [==============================] - 1s 4ms/step - loss: 0.4765 - acc: 0.7577 - val_loss: 0.4820 - val_acc: 0.7531
Epoch 14/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4770 - acc: 0.7625 - val_loss: 0.4848 - val_acc: 0.7460
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4762 - acc: 0.7577

254/254 [==============================] - 1s 5ms/step - loss: 0.4762 - acc: 0.7577 - val_loss: 0.5085 - val_acc: 0.7371
Epoch 16/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4746 - acc: 0.7625 - val_loss: 0.4858 - val_acc: 0.7549
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4740 - acc: 0.7609

254/254 [==============================] - 1s 4ms/step - loss: 0.4740 - acc: 0.7609 - val_loss: 0.4881 - val_acc: 0.7478
Epoch 18/30

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254/254 [==============================] - 1s 5ms/step - loss: 0.4750 - acc: 0.7554 - val_loss: 0.4937 - val_acc: 0.7442


Run completed: runs/2024-05-06T19-47-50Z

Training run 43/52 (flags = list(16, 32, 0.01, 20, 50, "tanh", "sigmoid", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-48-13Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

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WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0322s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0322s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6141 - acc: 0.6710

254/254 [==============================] - 3s 7ms/step - loss: 0.6141 - acc: 0.6710 - val_loss: 0.5376 - val_acc: 0.7318
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5380 - acc: 0.7234

254/254 [==============================] - 1s 4ms/step - loss: 0.5380 - acc: 0.7234 - val_loss: 0.4954 - val_acc: 0.7655
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5161 - acc: 0.7445

254/254 [==============================] - 1s 4ms/step - loss: 0.5161 - acc: 0.7445 - val_loss: 0.5144 - val_acc: 0.7460
Epoch 4/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5174 - acc: 0.7357

254/254 [==============================] - 1s 5ms/step - loss: 0.5174 - acc: 0.7357 - val_loss: 0.4941 - val_acc: 0.7531
Epoch 5/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5131 - acc: 0.7333

254/254 [==============================] - 1s 4ms/step - loss: 0.5131 - acc: 0.7333 - val_loss: 0.4819 - val_acc: 0.7567
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5037 - acc: 0.7394

254/254 [==============================] - 1s 4ms/step - loss: 0.5037 - acc: 0.7394 - val_loss: 0.4867 - val_acc: 0.7567
Epoch 7/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5049 - acc: 0.7477

254/254 [==============================] - 1s 5ms/step - loss: 0.5049 - acc: 0.7477 - val_loss: 0.5022 - val_acc: 0.7442
Epoch 8/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5028 - acc: 0.7469

254/254 [==============================] - 1s 4ms/step - loss: 0.5028 - acc: 0.7469 - val_loss: 0.4750 - val_acc: 0.7655
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4949 - acc: 0.7463

254/254 [==============================] - 1s 5ms/step - loss: 0.4949 - acc: 0.7463 - val_loss: 0.4889 - val_acc: 0.7531
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4982 - acc: 0.7483

254/254 [==============================] - 1s 4ms/step - loss: 0.4982 - acc: 0.7483 - val_loss: 0.4807 - val_acc: 0.7602
Epoch 11/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4916 - acc: 0.7510

254/254 [==============================] - 1s 4ms/step - loss: 0.4916 - acc: 0.7510 - val_loss: 0.4946 - val_acc: 0.7549
Epoch 12/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4951 - acc: 0.7546

254/254 [==============================] - 1s 5ms/step - loss: 0.4951 - acc: 0.7546 - val_loss: 0.4746 - val_acc: 0.7655
Epoch 13/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4903 - acc: 0.7506

254/254 [==============================] - 1s 4ms/step - loss: 0.4903 - acc: 0.7506 - val_loss: 0.4836 - val_acc: 0.7531
Epoch 14/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4910 - acc: 0.7524

254/254 [==============================] - 1s 5ms/step - loss: 0.4910 - acc: 0.7524 - val_loss: 0.4791 - val_acc: 0.7602
Epoch 15/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4869 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4869 - acc: 0.7524 - val_loss: 0.4710 - val_acc: 0.7673
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4944 - acc: 0.7441

254/254 [==============================] - 1s 4ms/step - loss: 0.4944 - acc: 0.7441 - val_loss: 0.4750 - val_acc: 0.7691
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4874 - acc: 0.7508

254/254 [==============================] - 1s 5ms/step - loss: 0.4874 - acc: 0.7508 - val_loss: 0.4774 - val_acc: 0.7638
Epoch 18/50

  1/254 [..............................] - ETA: 1s - loss: 0.5692 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4908 - acc: 0.7569

254/254 [==============================] - 1s 4ms/step - loss: 0.4908 - acc: 0.7569 - val_loss: 0.4827 - val_acc: 0.7549
Epoch 19/50

  1/254 [..............................] - ETA: 1s - loss: 0.3088 - acc: 0.8500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4923 - acc: 0.7591

254/254 [==============================] - 1s 4ms/step - loss: 0.4923 - acc: 0.7591 - val_loss: 0.5042 - val_acc: 0.7513
Epoch 20/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4918 - acc: 0.7483

254/254 [==============================] - 1s 4ms/step - loss: 0.4918 - acc: 0.7483 - val_loss: 0.4894 - val_acc: 0.7584
Epoch 21/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4877 - acc: 0.7524

254/254 [==============================] - 1s 4ms/step - loss: 0.4877 - acc: 0.7524 - val_loss: 0.4857 - val_acc: 0.7531
Epoch 22/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4879 - acc: 0.7514

254/254 [==============================] - 1s 4ms/step - loss: 0.4879 - acc: 0.7514 - val_loss: 0.4767 - val_acc: 0.7638
Epoch 23/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4872 - acc: 0.7520

254/254 [==============================] - 1s 4ms/step - loss: 0.4872 - acc: 0.7520 - val_loss: 0.4862 - val_acc: 0.7584
Epoch 24/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4859 - acc: 0.7571

254/254 [==============================] - 1s 4ms/step - loss: 0.4859 - acc: 0.7571 - val_loss: 0.4737 - val_acc: 0.7691
Epoch 25/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4868 - acc: 0.7568

254/254 [==============================] - 1s 5ms/step - loss: 0.4868 - acc: 0.7568 - val_loss: 0.4917 - val_acc: 0.7513


Run completed: runs/2024-05-06T19-48-13Z

Training run 44/52 (flags = list(16, 32, 0.01, 50, 30, "sigmoid", "tanh", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-48-42Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:02 - loss: 0.6856 - acc: 0.7000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.


 20/102 [====>.........................] - ETA: 0s - loss: 0.7065 - acc: 0.5860  
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102/102 [==============================] - 2s 3ms/step - loss: 0.6514 - acc: 0.6278

102/102 [==============================] - 2s 12ms/step - loss: 0.6514 - acc: 0.6278 - val_loss: 0.5701 - val_acc: 0.7176
Epoch 2/30

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102/102 [==============================] - 0s 4ms/step - loss: 0.5473 - acc: 0.7122

102/102 [==============================] - 1s 6ms/step - loss: 0.5473 - acc: 0.7122 - val_loss: 0.5096 - val_acc: 0.7513
Epoch 3/30

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102/102 [==============================] - 0s 4ms/step - loss: 0.5272 - acc: 0.7250

102/102 [==============================] - 1s 8ms/step - loss: 0.5272 - acc: 0.7250 - val_loss: 0.4952 - val_acc: 0.7584
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.4814 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.5177 - acc: 0.7353

102/102 [==============================] - 1s 6ms/step - loss: 0.5177 - acc: 0.7353 - val_loss: 0.4967 - val_acc: 0.7602
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.4400 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.5043 - acc: 0.7433

102/102 [==============================] - 1s 8ms/step - loss: 0.5043 - acc: 0.7433 - val_loss: 0.4718 - val_acc: 0.7673
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5188 - acc: 0.7400
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102/102 [==============================] - 0s 4ms/step - loss: 0.4988 - acc: 0.7463

102/102 [==============================] - 1s 10ms/step - loss: 0.4988 - acc: 0.7463 - val_loss: 0.4787 - val_acc: 0.7620
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.4816 - acc: 0.7400
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102/102 [==============================] - 0s 3ms/step - loss: 0.5012 - acc: 0.7445

102/102 [==============================] - 1s 6ms/step - loss: 0.5012 - acc: 0.7445 - val_loss: 0.4814 - val_acc: 0.7655
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.6318 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4960 - acc: 0.7463

102/102 [==============================] - 1s 6ms/step - loss: 0.4960 - acc: 0.7463 - val_loss: 0.5088 - val_acc: 0.7407
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.6670 - acc: 0.5800
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 72/102 [====================>.........] - ETA: 0s - loss: 0.5010 - acc: 0.7439
 89/102 [=========================>....] - ETA: 0s - loss: 0.5002 - acc: 0.7434
102/102 [==============================] - 0s 5ms/step - loss: 0.4982 - acc: 0.7457

102/102 [==============================] - 1s 7ms/step - loss: 0.4982 - acc: 0.7457 - val_loss: 0.4771 - val_acc: 0.7655
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.3966 - acc: 0.8600
 18/102 [====>.........................] - ETA: 0s - loss: 0.5027 - acc: 0.7311
 37/102 [=========>....................] - ETA: 0s - loss: 0.4939 - acc: 0.7351
 56/102 [===============>..............] - ETA: 0s - loss: 0.4958 - acc: 0.7414
 71/102 [===================>..........] - ETA: 0s - loss: 0.4938 - acc: 0.7386
 88/102 [========================>.....] - ETA: 0s - loss: 0.4945 - acc: 0.7434
102/102 [==============================] - 0s 3ms/step - loss: 0.4994 - acc: 0.7428

102/102 [==============================] - 1s 5ms/step - loss: 0.4994 - acc: 0.7428 - val_loss: 0.4896 - val_acc: 0.7549
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.6828 - acc: 0.6400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5206 - acc: 0.7389
 35/102 [=========>....................] - ETA: 0s - loss: 0.4847 - acc: 0.7457
 53/102 [==============>...............] - ETA: 0s - loss: 0.4924 - acc: 0.7525
 70/102 [===================>..........] - ETA: 0s - loss: 0.4994 - acc: 0.7497
 76/102 [=====================>........] - ETA: 0s - loss: 0.4933 - acc: 0.7532
 90/102 [=========================>....] - ETA: 0s - loss: 0.4970 - acc: 0.7504
102/102 [==============================] - 0s 3ms/step - loss: 0.4985 - acc: 0.7471

102/102 [==============================] - 1s 6ms/step - loss: 0.4985 - acc: 0.7471 - val_loss: 0.4939 - val_acc: 0.7549
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5822 - acc: 0.8000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5092 - acc: 0.7356
 36/102 [=========>....................] - ETA: 0s - loss: 0.4977 - acc: 0.7433
 54/102 [==============>...............] - ETA: 0s - loss: 0.4934 - acc: 0.7456
 71/102 [===================>..........] - ETA: 0s - loss: 0.4883 - acc: 0.7476
 89/102 [=========================>....] - ETA: 0s - loss: 0.4937 - acc: 0.7425
 99/102 [============================>.] - ETA: 0s - loss: 0.4928 - acc: 0.7432
102/102 [==============================] - 0s 3ms/step - loss: 0.4921 - acc: 0.7430

102/102 [==============================] - 1s 6ms/step - loss: 0.4921 - acc: 0.7430 - val_loss: 0.4903 - val_acc: 0.7549
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.3549 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4907 - acc: 0.7556
 37/102 [=========>....................] - ETA: 0s - loss: 0.4784 - acc: 0.7595
 55/102 [===============>..............] - ETA: 0s - loss: 0.4824 - acc: 0.7524
 71/102 [===================>..........] - ETA: 0s - loss: 0.4863 - acc: 0.7518
 87/102 [========================>.....] - ETA: 0s - loss: 0.4927 - acc: 0.7471
102/102 [==============================] - 0s 3ms/step - loss: 0.4947 - acc: 0.7504

102/102 [==============================] - 1s 6ms/step - loss: 0.4947 - acc: 0.7504 - val_loss: 0.4988 - val_acc: 0.7460
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.4898 - acc: 0.7600
 16/102 [===>..........................] - ETA: 0s - loss: 0.4893 - acc: 0.7350
 33/102 [========>.....................] - ETA: 0s - loss: 0.4914 - acc: 0.7412
 50/102 [=============>................] - ETA: 0s - loss: 0.4903 - acc: 0.7392
 64/102 [=================>............] - ETA: 0s - loss: 0.4954 - acc: 0.7403
 71/102 [===================>..........] - ETA: 0s - loss: 0.5006 - acc: 0.7375
 78/102 [=====================>........] - ETA: 0s - loss: 0.5001 - acc: 0.7387
 94/102 [==========================>...] - ETA: 0s - loss: 0.4936 - acc: 0.7415
102/102 [==============================] - 0s 4ms/step - loss: 0.4945 - acc: 0.7420

102/102 [==============================] - 1s 7ms/step - loss: 0.4945 - acc: 0.7420 - val_loss: 0.4868 - val_acc: 0.7567
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.3718 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5004 - acc: 0.7400
 37/102 [=========>....................] - ETA: 0s - loss: 0.5052 - acc: 0.7384
 52/102 [==============>...............] - ETA: 0s - loss: 0.4990 - acc: 0.7419
 69/102 [===================>..........] - ETA: 0s - loss: 0.4967 - acc: 0.7394
 86/102 [========================>.....] - ETA: 0s - loss: 0.4910 - acc: 0.7449
102/102 [==============================] - 0s 3ms/step - loss: 0.4921 - acc: 0.7447

102/102 [==============================] - 1s 6ms/step - loss: 0.4921 - acc: 0.7447 - val_loss: 0.4926 - val_acc: 0.7531


Run completed: runs/2024-05-06T19-48-42Z

Training run 45/52 (flags = list(16, 64, 0.01, 50, 50, "tanh", "sigmoid", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-48-55Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:00 - loss: 0.7277 - acc: 0.6400

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.


 17/102 [====>.........................] - ETA: 0s - loss: 0.7019 - acc: 0.5871  
 32/102 [========>.....................] - ETA: 0s - loss: 0.6896 - acc: 0.5781
 46/102 [============>.................] - ETA: 0s - loss: 0.6840 - acc: 0.5935
 62/102 [=================>............] - ETA: 0s - loss: 0.6669 - acc: 0.5990
 78/102 [=====================>........] - ETA: 0s - loss: 0.6555 - acc: 0.6223
 94/102 [==========================>...] - ETA: 0s - loss: 0.6435 - acc: 0.6362
102/102 [==============================] - 2s 3ms/step - loss: 0.6379 - acc: 0.6412

102/102 [==============================] - 2s 11ms/step - loss: 0.6379 - acc: 0.6412 - val_loss: 0.5323 - val_acc: 0.7407
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.4228 - acc: 0.8400
 14/102 [===>..........................] - ETA: 0s - loss: 0.5135 - acc: 0.7643
 23/102 [=====>........................] - ETA: 0s - loss: 0.5181 - acc: 0.7574
 39/102 [==========>...................] - ETA: 0s - loss: 0.5149 - acc: 0.7533
 51/102 [==============>...............] - ETA: 0s - loss: 0.5243 - acc: 0.7435
 65/102 [==================>...........] - ETA: 0s - loss: 0.5332 - acc: 0.7388
 80/102 [======================>.......] - ETA: 0s - loss: 0.5273 - acc: 0.7380
 95/102 [==========================>...] - ETA: 0s - loss: 0.5253 - acc: 0.7411
102/102 [==============================] - 0s 4ms/step - loss: 0.5279 - acc: 0.7388

102/102 [==============================] - 1s 6ms/step - loss: 0.5279 - acc: 0.7388 - val_loss: 0.5031 - val_acc: 0.7567
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.5239 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5120 - acc: 0.7600
 35/102 [=========>....................] - ETA: 0s - loss: 0.5172 - acc: 0.7474
 50/102 [=============>................] - ETA: 0s - loss: 0.5122 - acc: 0.7512
 66/102 [==================>...........] - ETA: 0s - loss: 0.5211 - acc: 0.7448
 82/102 [=======================>......] - ETA: 0s - loss: 0.5257 - acc: 0.7454
 85/102 [========================>.....] - ETA: 0s - loss: 0.5260 - acc: 0.7440
102/102 [==============================] - 0s 4ms/step - loss: 0.5204 - acc: 0.7439

102/102 [==============================] - 1s 7ms/step - loss: 0.5204 - acc: 0.7439 - val_loss: 0.4796 - val_acc: 0.7602
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.7448 - acc: 0.6400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5542 - acc: 0.7262
 27/102 [======>.......................] - ETA: 0s - loss: 0.5199 - acc: 0.7511
 43/102 [===========>..................] - ETA: 0s - loss: 0.5176 - acc: 0.7572
 53/102 [==============>...............] - ETA: 0s - loss: 0.5159 - acc: 0.7528
 65/102 [==================>...........] - ETA: 0s - loss: 0.5153 - acc: 0.7455
 75/102 [=====================>........] - ETA: 0s - loss: 0.5194 - acc: 0.7432
 91/102 [=========================>....] - ETA: 0s - loss: 0.5112 - acc: 0.7444
102/102 [==============================] - 0s 4ms/step - loss: 0.5165 - acc: 0.7461

102/102 [==============================] - 1s 7ms/step - loss: 0.5165 - acc: 0.7461 - val_loss: 0.4775 - val_acc: 0.7602
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.4350 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5213 - acc: 0.7475
 32/102 [========>.....................] - ETA: 0s - loss: 0.5015 - acc: 0.7556
 49/102 [=============>................] - ETA: 0s - loss: 0.4984 - acc: 0.7527
 65/102 [==================>...........] - ETA: 0s - loss: 0.4957 - acc: 0.7545
 80/102 [======================>.......] - ETA: 0s - loss: 0.5002 - acc: 0.7523
 93/102 [==========================>...] - ETA: 0s - loss: 0.5065 - acc: 0.7505
101/102 [============================>.] - ETA: 0s - loss: 0.5112 - acc: 0.7483
102/102 [==============================] - 0s 4ms/step - loss: 0.5116 - acc: 0.7477

102/102 [==============================] - 1s 8ms/step - loss: 0.5116 - acc: 0.7477 - val_loss: 0.5156 - val_acc: 0.7442
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.4211 - acc: 0.7800
 14/102 [===>..........................] - ETA: 0s - loss: 0.4855 - acc: 0.7629
 32/102 [========>.....................] - ETA: 0s - loss: 0.4853 - acc: 0.7581
 49/102 [=============>................] - ETA: 0s - loss: 0.5027 - acc: 0.7543
 64/102 [=================>............] - ETA: 0s - loss: 0.5052 - acc: 0.7556
 81/102 [======================>.......] - ETA: 0s - loss: 0.5011 - acc: 0.7560
 97/102 [===========================>..] - ETA: 0s - loss: 0.5064 - acc: 0.7536
102/102 [==============================] - 0s 3ms/step - loss: 0.5066 - acc: 0.7528

102/102 [==============================] - 1s 7ms/step - loss: 0.5066 - acc: 0.7528 - val_loss: 0.4898 - val_acc: 0.7602
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.5826 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5028 - acc: 0.7576
 34/102 [=========>....................] - ETA: 0s - loss: 0.4980 - acc: 0.7576
 49/102 [=============>................] - ETA: 0s - loss: 0.5013 - acc: 0.7596
 57/102 [===============>..............] - ETA: 0s - loss: 0.5047 - acc: 0.7586
 61/102 [================>.............] - ETA: 0s - loss: 0.5059 - acc: 0.7587
 74/102 [====================>.........] - ETA: 0s - loss: 0.5103 - acc: 0.7573
 90/102 [=========================>....] - ETA: 0s - loss: 0.5080 - acc: 0.7542
102/102 [==============================] - 0s 4ms/step - loss: 0.5056 - acc: 0.7554

102/102 [==============================] - 1s 6ms/step - loss: 0.5056 - acc: 0.7554 - val_loss: 0.4822 - val_acc: 0.7620
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.6243 - acc: 0.7600
 12/102 [==>...........................] - ETA: 0s - loss: 0.4987 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4858 - acc: 0.7744
 32/102 [========>.....................] - ETA: 0s - loss: 0.4906 - acc: 0.7656
 48/102 [=============>................] - ETA: 0s - loss: 0.4997 - acc: 0.7621
 59/102 [================>.............] - ETA: 0s - loss: 0.5058 - acc: 0.7569
 65/102 [==================>...........] - ETA: 0s - loss: 0.5065 - acc: 0.7557
 83/102 [=======================>......] - ETA: 0s - loss: 0.5063 - acc: 0.7513
 95/102 [==========================>...] - ETA: 0s - loss: 0.5061 - acc: 0.7507
102/102 [==============================] - 0s 5ms/step - loss: 0.5061 - acc: 0.7469

102/102 [==============================] - 1s 8ms/step - loss: 0.5061 - acc: 0.7469 - val_loss: 0.4970 - val_acc: 0.7584
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.4640 - acc: 0.7400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5291 - acc: 0.7425
 34/102 [=========>....................] - ETA: 0s - loss: 0.4988 - acc: 0.7588
 51/102 [==============>...............] - ETA: 0s - loss: 0.4983 - acc: 0.7592
 66/102 [==================>...........] - ETA: 0s - loss: 0.4960 - acc: 0.7588
 82/102 [=======================>......] - ETA: 0s - loss: 0.4936 - acc: 0.7554
100/102 [============================>.] - ETA: 0s - loss: 0.5036 - acc: 0.7520
102/102 [==============================] - 0s 3ms/step - loss: 0.5029 - acc: 0.7520

102/102 [==============================] - 1s 5ms/step - loss: 0.5029 - acc: 0.7520 - val_loss: 0.5071 - val_acc: 0.7531
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4968 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5251 - acc: 0.7387
 33/102 [========>.....................] - ETA: 0s - loss: 0.5291 - acc: 0.7255
 49/102 [=============>................] - ETA: 0s - loss: 0.5236 - acc: 0.7322
 65/102 [==================>...........] - ETA: 0s - loss: 0.5149 - acc: 0.7378
 77/102 [=====================>........] - ETA: 0s - loss: 0.5080 - acc: 0.7413
 93/102 [==========================>...] - ETA: 0s - loss: 0.5072 - acc: 0.7452
102/102 [==============================] - 0s 3ms/step - loss: 0.5101 - acc: 0.7433

102/102 [==============================] - 1s 6ms/step - loss: 0.5101 - acc: 0.7433 - val_loss: 0.4780 - val_acc: 0.7620
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.5453 - acc: 0.7400
 14/102 [===>..........................] - ETA: 0s - loss: 0.5396 - acc: 0.7500
 31/102 [========>.....................] - ETA: 0s - loss: 0.5274 - acc: 0.7387
 48/102 [=============>................] - ETA: 0s - loss: 0.5142 - acc: 0.7387
 65/102 [==================>...........] - ETA: 0s - loss: 0.5120 - acc: 0.7440
 80/102 [======================>.......] - ETA: 0s - loss: 0.5105 - acc: 0.7445
 94/102 [==========================>...] - ETA: 0s - loss: 0.5071 - acc: 0.7457
102/102 [==============================] - 0s 3ms/step - loss: 0.5078 - acc: 0.7447

102/102 [==============================] - 1s 6ms/step - loss: 0.5078 - acc: 0.7447 - val_loss: 0.4943 - val_acc: 0.7549
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.5758 - acc: 0.6800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5422 - acc: 0.7088
 34/102 [=========>....................] - ETA: 0s - loss: 0.5166 - acc: 0.7335
 51/102 [==============>...............] - ETA: 0s - loss: 0.5125 - acc: 0.7408
 66/102 [==================>...........] - ETA: 0s - loss: 0.5047 - acc: 0.7494
 82/102 [=======================>......] - ETA: 0s - loss: 0.5008 - acc: 0.7524
100/102 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.7510
102/102 [==============================] - 0s 3ms/step - loss: 0.5049 - acc: 0.7520

102/102 [==============================] - 1s 5ms/step - loss: 0.5049 - acc: 0.7520 - val_loss: 0.5063 - val_acc: 0.7478
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.5735 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5098 - acc: 0.7376
 26/102 [======>.......................] - ETA: 0s - loss: 0.5057 - acc: 0.7423
 35/102 [=========>....................] - ETA: 0s - loss: 0.5103 - acc: 0.7417
 46/102 [============>.................] - ETA: 0s - loss: 0.5164 - acc: 0.7422
 62/102 [=================>............] - ETA: 0s - loss: 0.5046 - acc: 0.7455
 74/102 [====================>.........] - ETA: 0s - loss: 0.5026 - acc: 0.7516
 88/102 [========================>.....] - ETA: 0s - loss: 0.5010 - acc: 0.7527
 96/102 [===========================>..] - ETA: 0s - loss: 0.4984 - acc: 0.7533
102/102 [==============================] - 1s 5ms/step - loss: 0.5047 - acc: 0.7504

102/102 [==============================] - 1s 8ms/step - loss: 0.5047 - acc: 0.7504 - val_loss: 0.5091 - val_acc: 0.7460
Epoch 14/50

  1/102 [..............................] - ETA: 0s - loss: 0.5205 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5098 - acc: 0.7256
 36/102 [=========>....................] - ETA: 0s - loss: 0.5013 - acc: 0.7356
 54/102 [==============>...............] - ETA: 0s - loss: 0.5002 - acc: 0.7474
 70/102 [===================>..........] - ETA: 0s - loss: 0.4999 - acc: 0.7486
 86/102 [========================>.....] - ETA: 0s - loss: 0.5078 - acc: 0.7460
102/102 [==============================] - 0s 3ms/step - loss: 0.5038 - acc: 0.7441

102/102 [==============================] - 1s 5ms/step - loss: 0.5038 - acc: 0.7441 - val_loss: 0.5004 - val_acc: 0.7460


Run completed: runs/2024-05-06T19-48-55Z

Training run 46/52 (flags = list(16, 32, 0.01, 50, 50, "sigmoid", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-49-07Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/102 [..............................] - ETA: 2:51 - loss: 0.7337 - acc: 0.2600

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.1130s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.1130s). Check your callbacks.


 17/102 [====>.........................] - ETA: 0s - loss: 0.7164 - acc: 0.4671  
 35/102 [=========>....................] - ETA: 0s - loss: 0.7000 - acc: 0.5051
 53/102 [==============>...............] - ETA: 0s - loss: 0.6962 - acc: 0.5253
 63/102 [=================>............] - ETA: 0s - loss: 0.6910 - acc: 0.5305
 80/102 [======================>.......] - ETA: 0s - loss: 0.6853 - acc: 0.5437
 96/102 [===========================>..] - ETA: 0s - loss: 0.6805 - acc: 0.5504
102/102 [==============================] - 2s 4ms/step - loss: 0.6805 - acc: 0.5525

102/102 [==============================] - 3s 12ms/step - loss: 0.6805 - acc: 0.5525 - val_loss: 0.6237 - val_acc: 0.7229
Epoch 2/50

  1/102 [..............................] - ETA: 0s - loss: 0.6059 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.6333 - acc: 0.6141
 28/102 [=======>......................] - ETA: 0s - loss: 0.6316 - acc: 0.6314
 34/102 [=========>....................] - ETA: 0s - loss: 0.6330 - acc: 0.6347
 50/102 [=============>................] - ETA: 0s - loss: 0.6217 - acc: 0.6396
 68/102 [===================>..........] - ETA: 0s - loss: 0.6164 - acc: 0.6476
 77/102 [=====================>........] - ETA: 0s - loss: 0.6134 - acc: 0.6532
 78/102 [=====================>........] - ETA: 0s - loss: 0.6133 - acc: 0.6528
 92/102 [==========================>...] - ETA: 0s - loss: 0.6085 - acc: 0.6580
102/102 [==============================] - 0s 4ms/step - loss: 0.6049 - acc: 0.6635

102/102 [==============================] - 1s 6ms/step - loss: 0.6049 - acc: 0.6635 - val_loss: 0.5375 - val_acc: 0.7602
Epoch 3/50

  1/102 [..............................] - ETA: 0s - loss: 0.5740 - acc: 0.6200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5560 - acc: 0.7011
 36/102 [=========>....................] - ETA: 0s - loss: 0.5707 - acc: 0.7011
 53/102 [==============>...............] - ETA: 0s - loss: 0.5709 - acc: 0.7034
 60/102 [================>.............] - ETA: 0s - loss: 0.5688 - acc: 0.7067
 77/102 [=====================>........] - ETA: 0s - loss: 0.5677 - acc: 0.7094
 86/102 [========================>.....] - ETA: 0s - loss: 0.5663 - acc: 0.7119
102/102 [==============================] - 0s 3ms/step - loss: 0.5649 - acc: 0.7134

102/102 [==============================] - 1s 7ms/step - loss: 0.5649 - acc: 0.7134 - val_loss: 0.5033 - val_acc: 0.7584
Epoch 4/50

  1/102 [..............................] - ETA: 0s - loss: 0.5349 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.6092 - acc: 0.7178
 37/102 [=========>....................] - ETA: 0s - loss: 0.5756 - acc: 0.7249
 54/102 [==============>...............] - ETA: 0s - loss: 0.5664 - acc: 0.7267
 70/102 [===================>..........] - ETA: 0s - loss: 0.5593 - acc: 0.7300
 85/102 [========================>.....] - ETA: 0s - loss: 0.5590 - acc: 0.7278
102/102 [==============================] - 0s 3ms/step - loss: 0.5556 - acc: 0.7292

102/102 [==============================] - 1s 6ms/step - loss: 0.5556 - acc: 0.7292 - val_loss: 0.4958 - val_acc: 0.7602
Epoch 5/50

  1/102 [..............................] - ETA: 0s - loss: 0.5474 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5426 - acc: 0.7267
 36/102 [=========>....................] - ETA: 0s - loss: 0.5480 - acc: 0.7306
 54/102 [==============>...............] - ETA: 0s - loss: 0.5455 - acc: 0.7359
 72/102 [====================>.........] - ETA: 0s - loss: 0.5359 - acc: 0.7458
 88/102 [========================>.....] - ETA: 0s - loss: 0.5314 - acc: 0.7441
102/102 [==============================] - 0s 3ms/step - loss: 0.5339 - acc: 0.7428

102/102 [==============================] - 1s 6ms/step - loss: 0.5339 - acc: 0.7428 - val_loss: 0.4935 - val_acc: 0.7602
Epoch 6/50

  1/102 [..............................] - ETA: 0s - loss: 0.5591 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5488 - acc: 0.7318
 35/102 [=========>....................] - ETA: 0s - loss: 0.5298 - acc: 0.7331
 51/102 [==============>...............] - ETA: 0s - loss: 0.5319 - acc: 0.7416
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 84/102 [=======================>......] - ETA: 0s - loss: 0.5276 - acc: 0.7498
 99/102 [============================>.] - ETA: 0s - loss: 0.5254 - acc: 0.7501
102/102 [==============================] - 0s 3ms/step - loss: 0.5258 - acc: 0.7487

102/102 [==============================] - 1s 7ms/step - loss: 0.5258 - acc: 0.7487 - val_loss: 0.4946 - val_acc: 0.7567
Epoch 7/50

  1/102 [..............................] - ETA: 0s - loss: 0.7400 - acc: 0.7200
 18/102 [====>.........................] - ETA: 0s - loss: 0.5315 - acc: 0.7411
 36/102 [=========>....................] - ETA: 0s - loss: 0.5266 - acc: 0.7378
 44/102 [===========>..................] - ETA: 0s - loss: 0.5290 - acc: 0.7400
 58/102 [================>.............] - ETA: 0s - loss: 0.5245 - acc: 0.7428
 76/102 [=====================>........] - ETA: 0s - loss: 0.5267 - acc: 0.7439
 93/102 [==========================>...] - ETA: 0s - loss: 0.5224 - acc: 0.7454
102/102 [==============================] - 0s 3ms/step - loss: 0.5241 - acc: 0.7467

102/102 [==============================] - 1s 6ms/step - loss: 0.5241 - acc: 0.7467 - val_loss: 0.4812 - val_acc: 0.7673
Epoch 8/50

  1/102 [..............................] - ETA: 0s - loss: 0.6406 - acc: 0.6200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5259 - acc: 0.7271
 36/102 [=========>....................] - ETA: 0s - loss: 0.5262 - acc: 0.7367
 54/102 [==============>...............] - ETA: 0s - loss: 0.5051 - acc: 0.7478
 71/102 [===================>..........] - ETA: 0s - loss: 0.5062 - acc: 0.7501
 88/102 [========================>.....] - ETA: 0s - loss: 0.5099 - acc: 0.7493
102/102 [==============================] - 0s 3ms/step - loss: 0.5105 - acc: 0.7479

102/102 [==============================] - 1s 7ms/step - loss: 0.5105 - acc: 0.7479 - val_loss: 0.4865 - val_acc: 0.7584
Epoch 9/50

  1/102 [..............................] - ETA: 0s - loss: 0.4490 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5318 - acc: 0.7424
 35/102 [=========>....................] - ETA: 0s - loss: 0.5063 - acc: 0.7600
 51/102 [==============>...............] - ETA: 0s - loss: 0.5085 - acc: 0.7537
 68/102 [===================>..........] - ETA: 0s - loss: 0.5158 - acc: 0.7479
 86/102 [========================>.....] - ETA: 0s - loss: 0.5172 - acc: 0.7500
102/102 [==============================] - 0s 3ms/step - loss: 0.5105 - acc: 0.7520

102/102 [==============================] - 1s 6ms/step - loss: 0.5105 - acc: 0.7520 - val_loss: 0.4888 - val_acc: 0.7584
Epoch 10/50

  1/102 [..............................] - ETA: 0s - loss: 0.4601 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.5412 - acc: 0.7222
 37/102 [=========>....................] - ETA: 0s - loss: 0.5254 - acc: 0.7378
 55/102 [===============>..............] - ETA: 0s - loss: 0.5145 - acc: 0.7433
 69/102 [===================>..........] - ETA: 0s - loss: 0.5167 - acc: 0.7467
 85/102 [========================>.....] - ETA: 0s - loss: 0.5176 - acc: 0.7452
 96/102 [===========================>..] - ETA: 0s - loss: 0.5150 - acc: 0.7458
102/102 [==============================] - 0s 3ms/step - loss: 0.5154 - acc: 0.7453

102/102 [==============================] - 1s 6ms/step - loss: 0.5154 - acc: 0.7453 - val_loss: 0.4764 - val_acc: 0.7638
Epoch 11/50

  1/102 [..............................] - ETA: 0s - loss: 0.4584 - acc: 0.8200
 19/102 [====>.........................] - ETA: 0s - loss: 0.5421 - acc: 0.7284
 34/102 [=========>....................] - ETA: 0s - loss: 0.5231 - acc: 0.7406
 52/102 [==============>...............] - ETA: 0s - loss: 0.5185 - acc: 0.7404
 69/102 [===================>..........] - ETA: 0s - loss: 0.5211 - acc: 0.7394
 85/102 [========================>.....] - ETA: 0s - loss: 0.5203 - acc: 0.7414
102/102 [==============================] - 0s 3ms/step - loss: 0.5079 - acc: 0.7506

102/102 [==============================] - 1s 5ms/step - loss: 0.5079 - acc: 0.7506 - val_loss: 0.4790 - val_acc: 0.7673
Epoch 12/50

  1/102 [..............................] - ETA: 0s - loss: 0.5237 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5462 - acc: 0.7489
 36/102 [=========>....................] - ETA: 0s - loss: 0.5168 - acc: 0.7506
 54/102 [==============>...............] - ETA: 0s - loss: 0.5068 - acc: 0.7478
 73/102 [====================>.........] - ETA: 0s - loss: 0.5124 - acc: 0.7485
 91/102 [=========================>....] - ETA: 0s - loss: 0.5092 - acc: 0.7505
102/102 [==============================] - 0s 3ms/step - loss: 0.5109 - acc: 0.7500

102/102 [==============================] - 1s 5ms/step - loss: 0.5109 - acc: 0.7500 - val_loss: 0.4807 - val_acc: 0.7691
Epoch 13/50

  1/102 [..............................] - ETA: 0s - loss: 0.5509 - acc: 0.6800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5079 - acc: 0.7612
 34/102 [=========>....................] - ETA: 0s - loss: 0.5175 - acc: 0.7494
 52/102 [==============>...............] - ETA: 0s - loss: 0.5047 - acc: 0.7531
 68/102 [===================>..........] - ETA: 0s - loss: 0.5129 - acc: 0.7450
 84/102 [=======================>......] - ETA: 0s - loss: 0.5124 - acc: 0.7443
102/102 [==============================] - 0s 3ms/step - loss: 0.5064 - acc: 0.7469

102/102 [==============================] - 1s 6ms/step - loss: 0.5064 - acc: 0.7469 - val_loss: 0.4790 - val_acc: 0.7673
Epoch 14/50

  1/102 [..............................] - ETA: 1s - loss: 0.5364 - acc: 0.7800
 18/102 [====>.........................] - ETA: 0s - loss: 0.5158 - acc: 0.7533
 33/102 [========>.....................] - ETA: 0s - loss: 0.5107 - acc: 0.7521
 51/102 [==============>...............] - ETA: 0s - loss: 0.5119 - acc: 0.7424
 55/102 [===============>..............] - ETA: 0s - loss: 0.5052 - acc: 0.7440
 72/102 [====================>.........] - ETA: 0s - loss: 0.5103 - acc: 0.7450
 76/102 [=====================>........] - ETA: 0s - loss: 0.5056 - acc: 0.7476
 87/102 [========================>.....] - ETA: 0s - loss: 0.5076 - acc: 0.7453
102/102 [==============================] - 0s 4ms/step - loss: 0.5065 - acc: 0.7465

102/102 [==============================] - 1s 7ms/step - loss: 0.5065 - acc: 0.7465 - val_loss: 0.4879 - val_acc: 0.7513
Epoch 15/50

  1/102 [..............................] - ETA: 0s - loss: 0.5149 - acc: 0.8200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5060 - acc: 0.7353
 34/102 [=========>....................] - ETA: 0s - loss: 0.5102 - acc: 0.7371
 45/102 [============>.................] - ETA: 0s - loss: 0.4976 - acc: 0.7444
 53/102 [==============>...............] - ETA: 0s - loss: 0.5071 - acc: 0.7415
 67/102 [==================>...........] - ETA: 0s - loss: 0.5068 - acc: 0.7442
 83/102 [=======================>......] - ETA: 0s - loss: 0.5001 - acc: 0.7506
 92/102 [==========================>...] - ETA: 0s - loss: 0.5012 - acc: 0.7498
102/102 [==============================] - 0s 4ms/step - loss: 0.5011 - acc: 0.7497

102/102 [==============================] - 1s 7ms/step - loss: 0.5011 - acc: 0.7497 - val_loss: 0.4770 - val_acc: 0.7655
Epoch 16/50

  1/102 [..............................] - ETA: 0s - loss: 0.5925 - acc: 0.6800
 18/102 [====>.........................] - ETA: 0s - loss: 0.4950 - acc: 0.7367
 37/102 [=========>....................] - ETA: 0s - loss: 0.4895 - acc: 0.7454
 55/102 [===============>..............] - ETA: 0s - loss: 0.5027 - acc: 0.7411
 73/102 [====================>.........] - ETA: 0s - loss: 0.5016 - acc: 0.7403
 92/102 [==========================>...] - ETA: 0s - loss: 0.4992 - acc: 0.7437
102/102 [==============================] - 0s 3ms/step - loss: 0.4965 - acc: 0.7469

102/102 [==============================] - 1s 6ms/step - loss: 0.4965 - acc: 0.7469 - val_loss: 0.4864 - val_acc: 0.7549
Epoch 17/50

  1/102 [..............................] - ETA: 0s - loss: 0.4819 - acc: 0.7800
 17/102 [====>.........................] - ETA: 0s - loss: 0.5192 - acc: 0.7435
 34/102 [=========>....................] - ETA: 0s - loss: 0.5256 - acc: 0.7329
 51/102 [==============>...............] - ETA: 0s - loss: 0.5193 - acc: 0.7427
 69/102 [===================>..........] - ETA: 0s - loss: 0.5158 - acc: 0.7383
 85/102 [========================>.....] - ETA: 0s - loss: 0.5088 - acc: 0.7428
102/102 [==============================] - 0s 3ms/step - loss: 0.5039 - acc: 0.7457

102/102 [==============================] - 1s 6ms/step - loss: 0.5039 - acc: 0.7457 - val_loss: 0.4800 - val_acc: 0.7584
Epoch 18/50

  1/102 [..............................] - ETA: 0s - loss: 0.3528 - acc: 0.8400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4916 - acc: 0.7667
 36/102 [=========>....................] - ETA: 0s - loss: 0.4861 - acc: 0.7733
 51/102 [==============>...............] - ETA: 0s - loss: 0.4951 - acc: 0.7584
 68/102 [===================>..........] - ETA: 0s - loss: 0.5000 - acc: 0.7574
 84/102 [=======================>......] - ETA: 0s - loss: 0.4959 - acc: 0.7567
102/102 [==============================] - 0s 3ms/step - loss: 0.4986 - acc: 0.7548

102/102 [==============================] - 1s 6ms/step - loss: 0.4986 - acc: 0.7548 - val_loss: 0.4789 - val_acc: 0.7584
Epoch 19/50

  1/102 [..............................] - ETA: 0s - loss: 0.3852 - acc: 0.7800
 13/102 [==>...........................] - ETA: 0s - loss: 0.4684 - acc: 0.7554
 17/102 [====>.........................] - ETA: 0s - loss: 0.4834 - acc: 0.7541
 30/102 [=======>......................] - ETA: 0s - loss: 0.5206 - acc: 0.7380
 47/102 [============>.................] - ETA: 0s - loss: 0.5084 - acc: 0.7400
 65/102 [==================>...........] - ETA: 0s - loss: 0.5011 - acc: 0.7375
 83/102 [=======================>......] - ETA: 0s - loss: 0.5076 - acc: 0.7434
102/102 [==============================] - 0s 4ms/step - loss: 0.5023 - acc: 0.7418

102/102 [==============================] - 1s 7ms/step - loss: 0.5023 - acc: 0.7418 - val_loss: 0.4865 - val_acc: 0.7531
Epoch 20/50

  1/102 [..............................] - ETA: 0s - loss: 0.3748 - acc: 0.9000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4818 - acc: 0.7522
 37/102 [=========>....................] - ETA: 0s - loss: 0.4798 - acc: 0.7605
 55/102 [===============>..............] - ETA: 0s - loss: 0.4879 - acc: 0.7582
 72/102 [====================>.........] - ETA: 0s - loss: 0.4963 - acc: 0.7533
 84/102 [=======================>......] - ETA: 0s - loss: 0.4938 - acc: 0.7548
 94/102 [==========================>...] - ETA: 0s - loss: 0.4979 - acc: 0.7519
102/102 [==============================] - 0s 4ms/step - loss: 0.4969 - acc: 0.7491

102/102 [==============================] - 1s 6ms/step - loss: 0.4969 - acc: 0.7491 - val_loss: 0.4852 - val_acc: 0.7531


Run completed: runs/2024-05-06T19-49-07Z

Training run 47/52 (flags = list(16, 32, 0.01, 50, 30, "relu", "relu", 0.2, 0.2)) 
Using run directory runs/2024-05-06T19-49-23Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 2:00 - loss: 0.7955 - acc: 0.4200

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0288s). Check your callbacks.


 18/102 [====>.........................] - ETA: 0s - loss: 0.6758 - acc: 0.6056  
 37/102 [=========>....................] - ETA: 0s - loss: 0.6453 - acc: 0.6389
 54/102 [==============>...............] - ETA: 0s - loss: 0.6197 - acc: 0.6578
 73/102 [====================>.........] - ETA: 0s - loss: 0.6047 - acc: 0.6753
 91/102 [=========================>....] - ETA: 0s - loss: 0.5893 - acc: 0.6873
102/102 [==============================] - 1s 3ms/step - loss: 0.5889 - acc: 0.6870

102/102 [==============================] - 2s 11ms/step - loss: 0.5889 - acc: 0.6870 - val_loss: 0.4955 - val_acc: 0.7318
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.5552 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5363 - acc: 0.7256
 34/102 [=========>....................] - ETA: 0s - loss: 0.5398 - acc: 0.7259
 51/102 [==============>...............] - ETA: 0s - loss: 0.5328 - acc: 0.7263
 57/102 [===============>..............] - ETA: 0s - loss: 0.5280 - acc: 0.7281
 67/102 [==================>...........] - ETA: 0s - loss: 0.5300 - acc: 0.7269
 81/102 [======================>.......] - ETA: 0s - loss: 0.5304 - acc: 0.7286
 97/102 [===========================>..] - ETA: 0s - loss: 0.5265 - acc: 0.7249
102/102 [==============================] - 0s 4ms/step - loss: 0.5264 - acc: 0.7248

102/102 [==============================] - 1s 6ms/step - loss: 0.5264 - acc: 0.7248 - val_loss: 0.4878 - val_acc: 0.7496
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.3407 - acc: 0.8200
  8/102 [=>............................] - ETA: 1s - loss: 0.5278 - acc: 0.7250
 15/102 [===>..........................] - ETA: 0s - loss: 0.5561 - acc: 0.6947
 32/102 [========>.....................] - ETA: 0s - loss: 0.5310 - acc: 0.7081
 38/102 [==========>...................] - ETA: 0s - loss: 0.5297 - acc: 0.7058
 56/102 [===============>..............] - ETA: 0s - loss: 0.5219 - acc: 0.7161
 58/102 [================>.............] - ETA: 0s - loss: 0.5241 - acc: 0.7162
 76/102 [=====================>........] - ETA: 0s - loss: 0.5198 - acc: 0.7239
 86/102 [========================>.....] - ETA: 0s - loss: 0.5142 - acc: 0.7323
102/102 [==============================] - 0s 5ms/step - loss: 0.5147 - acc: 0.7349

102/102 [==============================] - 1s 8ms/step - loss: 0.5147 - acc: 0.7349 - val_loss: 0.4872 - val_acc: 0.7531
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.6116 - acc: 0.6400
 18/102 [====>.........................] - ETA: 0s - loss: 0.5012 - acc: 0.7300
 37/102 [=========>....................] - ETA: 0s - loss: 0.4988 - acc: 0.7351
 54/102 [==============>...............] - ETA: 0s - loss: 0.5117 - acc: 0.7326
 66/102 [==================>...........] - ETA: 0s - loss: 0.5202 - acc: 0.7239
 83/102 [=======================>......] - ETA: 0s - loss: 0.5134 - acc: 0.7277
 93/102 [==========================>...] - ETA: 0s - loss: 0.5133 - acc: 0.7288
102/102 [==============================] - 0s 3ms/step - loss: 0.5102 - acc: 0.7317

102/102 [==============================] - 1s 6ms/step - loss: 0.5102 - acc: 0.7317 - val_loss: 0.4731 - val_acc: 0.7655
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.4475 - acc: 0.7400
 18/102 [====>.........................] - ETA: 0s - loss: 0.4819 - acc: 0.7433
 37/102 [=========>....................] - ETA: 0s - loss: 0.4972 - acc: 0.7454
 55/102 [===============>..............] - ETA: 0s - loss: 0.4855 - acc: 0.7502
 71/102 [===================>..........] - ETA: 0s - loss: 0.4980 - acc: 0.7439
 86/102 [========================>.....] - ETA: 0s - loss: 0.5059 - acc: 0.7419
 97/102 [===========================>..] - ETA: 0s - loss: 0.5024 - acc: 0.7410
102/102 [==============================] - 0s 3ms/step - loss: 0.5027 - acc: 0.7422

102/102 [==============================] - 1s 6ms/step - loss: 0.5027 - acc: 0.7422 - val_loss: 0.4940 - val_acc: 0.7442
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5740 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5098 - acc: 0.7375
 34/102 [=========>....................] - ETA: 0s - loss: 0.5001 - acc: 0.7241
 51/102 [==============>...............] - ETA: 0s - loss: 0.4969 - acc: 0.7227
 68/102 [===================>..........] - ETA: 0s - loss: 0.4868 - acc: 0.7412
 86/102 [========================>.....] - ETA: 0s - loss: 0.4958 - acc: 0.7423
102/102 [==============================] - 0s 4ms/step - loss: 0.4970 - acc: 0.7398

102/102 [==============================] - 1s 5ms/step - loss: 0.4970 - acc: 0.7398 - val_loss: 0.4982 - val_acc: 0.7478
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5067 - acc: 0.7400
 17/102 [====>.........................] - ETA: 0s - loss: 0.5124 - acc: 0.7082
 30/102 [=======>......................] - ETA: 0s - loss: 0.5054 - acc: 0.7213
 47/102 [============>.................] - ETA: 0s - loss: 0.4955 - acc: 0.7366
 63/102 [=================>............] - ETA: 0s - loss: 0.4985 - acc: 0.7435
 80/102 [======================>.......] - ETA: 0s - loss: 0.4923 - acc: 0.7462
 93/102 [==========================>...] - ETA: 0s - loss: 0.4970 - acc: 0.7439
102/102 [==============================] - 0s 4ms/step - loss: 0.4946 - acc: 0.7455

102/102 [==============================] - 1s 6ms/step - loss: 0.4946 - acc: 0.7455 - val_loss: 0.4873 - val_acc: 0.7513
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.6367 - acc: 0.7200
  4/102 [>.............................] - ETA: 2s - loss: 0.5454 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5088 - acc: 0.7387
 34/102 [=========>....................] - ETA: 0s - loss: 0.5050 - acc: 0.7424
 52/102 [==============>...............] - ETA: 0s - loss: 0.4976 - acc: 0.7465
 67/102 [==================>...........] - ETA: 0s - loss: 0.4960 - acc: 0.7510
 86/102 [========================>.....] - ETA: 0s - loss: 0.4911 - acc: 0.7533
102/102 [==============================] - 0s 4ms/step - loss: 0.4916 - acc: 0.7508

102/102 [==============================] - 1s 7ms/step - loss: 0.4916 - acc: 0.7508 - val_loss: 0.4880 - val_acc: 0.7513
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.4939 - acc: 0.7000
 18/102 [====>.........................] - ETA: 0s - loss: 0.4991 - acc: 0.7456
 37/102 [=========>....................] - ETA: 0s - loss: 0.4873 - acc: 0.7400
 56/102 [===============>..............] - ETA: 0s - loss: 0.4919 - acc: 0.7389
 73/102 [====================>.........] - ETA: 0s - loss: 0.4921 - acc: 0.7378
 91/102 [=========================>....] - ETA: 0s - loss: 0.4934 - acc: 0.7400
102/102 [==============================] - 0s 3ms/step - loss: 0.4901 - acc: 0.7433

102/102 [==============================] - 1s 5ms/step - loss: 0.4901 - acc: 0.7433 - val_loss: 0.4957 - val_acc: 0.7442
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.4191 - acc: 0.8400
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102/102 [==============================] - 0s 3ms/step - loss: 0.4837 - acc: 0.7583

102/102 [==============================] - 1s 5ms/step - loss: 0.4837 - acc: 0.7583 - val_loss: 0.5015 - val_acc: 0.7496
Epoch 11/30

  1/102 [..............................] - ETA: 1s - loss: 0.5204 - acc: 0.7600
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102/102 [==============================] - 0s 3ms/step - loss: 0.4905 - acc: 0.7447

102/102 [==============================] - 1s 7ms/step - loss: 0.4905 - acc: 0.7447 - val_loss: 0.4910 - val_acc: 0.7442
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.4245 - acc: 0.7000
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102/102 [==============================] - 0s 3ms/step - loss: 0.4868 - acc: 0.7475

102/102 [==============================] - 1s 6ms/step - loss: 0.4868 - acc: 0.7475 - val_loss: 0.4822 - val_acc: 0.7549
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.4455 - acc: 0.8200
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101/102 [============================>.] - ETA: 0s - loss: 0.4865 - acc: 0.7495
102/102 [==============================] - 0s 3ms/step - loss: 0.4868 - acc: 0.7493

102/102 [==============================] - 1s 6ms/step - loss: 0.4868 - acc: 0.7493 - val_loss: 0.4888 - val_acc: 0.7478
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.3583 - acc: 0.8600
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102/102 [==============================] - 0s 4ms/step - loss: 0.4831 - acc: 0.7530

102/102 [==============================] - 1s 7ms/step - loss: 0.4831 - acc: 0.7530 - val_loss: 0.4790 - val_acc: 0.7709


Run completed: runs/2024-05-06T19-49-23Z

Training run 48/52 (flags = list(32, 32, 0.001, 20, 30, "relu", "tanh", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-49-35Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:02 - loss: 0.7635 - acc: 0.3500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0291s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0291s). Check your callbacks.


 16/254 [>.............................] - ETA: 0s - loss: 0.7108 - acc: 0.5219  
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254/254 [==============================] - 2s 3ms/step - loss: 0.5640 - acc: 0.6966

254/254 [==============================] - 3s 6ms/step - loss: 0.5640 - acc: 0.6966 - val_loss: 0.4597 - val_acc: 0.7780
Epoch 2/30

  1/254 [..............................] - ETA: 1s - loss: 0.4057 - acc: 0.8500
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254/254 [==============================] - 1s 4ms/step - loss: 0.5144 - acc: 0.7435

254/254 [==============================] - 1s 6ms/step - loss: 0.5144 - acc: 0.7435 - val_loss: 0.4951 - val_acc: 0.7478
Epoch 3/30

  1/254 [..............................] - ETA: 0s - loss: 0.5646 - acc: 0.6000
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254/254 [==============================] - 1s 4ms/step - loss: 0.5024 - acc: 0.7382

254/254 [==============================] - 2s 8ms/step - loss: 0.5024 - acc: 0.7382 - val_loss: 0.4833 - val_acc: 0.7638
Epoch 4/30

  1/254 [..............................] - ETA: 0s - loss: 0.4818 - acc: 0.7000
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254/254 [==============================] - 1s 4ms/step - loss: 0.4995 - acc: 0.7548

254/254 [==============================] - 2s 7ms/step - loss: 0.4995 - acc: 0.7548 - val_loss: 0.4943 - val_acc: 0.7496
Epoch 5/30

  1/254 [..............................] - ETA: 0s - loss: 0.4230 - acc: 0.5500
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254/254 [==============================] - 1s 4ms/step - loss: 0.4838 - acc: 0.7538

254/254 [==============================] - 2s 6ms/step - loss: 0.4838 - acc: 0.7538 - val_loss: 0.4952 - val_acc: 0.7442
Epoch 6/30

  1/254 [..............................] - ETA: 0s - loss: 0.3411 - acc: 0.7000
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254/254 [==============================] - 1s 4ms/step - loss: 0.4945 - acc: 0.7426

254/254 [==============================] - 2s 7ms/step - loss: 0.4945 - acc: 0.7426 - val_loss: 0.4977 - val_acc: 0.7442
Epoch 7/30

  1/254 [..............................] - ETA: 1s - loss: 0.4001 - acc: 0.6500
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254/254 [==============================] - 1s 4ms/step - loss: 0.4869 - acc: 0.7524

254/254 [==============================] - 2s 7ms/step - loss: 0.4869 - acc: 0.7524 - val_loss: 0.4826 - val_acc: 0.7584
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4863 - acc: 0.7493

254/254 [==============================] - 1s 5ms/step - loss: 0.4863 - acc: 0.7493 - val_loss: 0.4907 - val_acc: 0.7407
Epoch 9/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4780 - acc: 0.7532

254/254 [==============================] - 1s 4ms/step - loss: 0.4780 - acc: 0.7532 - val_loss: 0.4899 - val_acc: 0.7602
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4822 - acc: 0.7479

254/254 [==============================] - 1s 4ms/step - loss: 0.4822 - acc: 0.7479 - val_loss: 0.4796 - val_acc: 0.7620
Epoch 11/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4702 - acc: 0.7662

254/254 [==============================] - 1s 4ms/step - loss: 0.4702 - acc: 0.7662 - val_loss: 0.4811 - val_acc: 0.7726


Run completed: runs/2024-05-06T19-49-35Z

Training run 49/52 (flags = list(32, 16, 0.01, 20, 50, "relu", "tanh", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-49-53Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:05 - loss: 0.5304 - acc: 0.7500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0289s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.0289s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.5968 - acc: 0.6874

254/254 [==============================] - 3s 7ms/step - loss: 0.5968 - acc: 0.6874 - val_loss: 0.4683 - val_acc: 0.7833
Epoch 2/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5251 - acc: 0.7370

254/254 [==============================] - 1s 4ms/step - loss: 0.5251 - acc: 0.7370 - val_loss: 0.4774 - val_acc: 0.7691
Epoch 3/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.5089 - acc: 0.7388

254/254 [==============================] - 1s 5ms/step - loss: 0.5089 - acc: 0.7388 - val_loss: 0.4712 - val_acc: 0.7620
Epoch 4/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4995 - acc: 0.7439

254/254 [==============================] - 1s 4ms/step - loss: 0.4995 - acc: 0.7439 - val_loss: 0.4754 - val_acc: 0.7673
Epoch 5/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.5005 - acc: 0.7504

254/254 [==============================] - 1s 5ms/step - loss: 0.5005 - acc: 0.7504 - val_loss: 0.4779 - val_acc: 0.7620
Epoch 6/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4976 - acc: 0.7497

254/254 [==============================] - 1s 4ms/step - loss: 0.4976 - acc: 0.7497 - val_loss: 0.4800 - val_acc: 0.7655
Epoch 7/50

  1/254 [..............................] - ETA: 1s - loss: 0.3574 - acc: 0.9000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4885 - acc: 0.7564

254/254 [==============================] - 1s 4ms/step - loss: 0.4885 - acc: 0.7564 - val_loss: 0.4914 - val_acc: 0.7567
Epoch 8/50

  1/254 [..............................] - ETA: 1s - loss: 0.4631 - acc: 0.7500
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254/254 [==============================] - 1s 4ms/step - loss: 0.4898 - acc: 0.7524

254/254 [==============================] - 1s 5ms/step - loss: 0.4898 - acc: 0.7524 - val_loss: 0.4719 - val_acc: 0.7709
Epoch 9/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4859 - acc: 0.7548

254/254 [==============================] - 1s 4ms/step - loss: 0.4859 - acc: 0.7548 - val_loss: 0.4857 - val_acc: 0.7531
Epoch 10/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4827 - acc: 0.7526

254/254 [==============================] - 1s 4ms/step - loss: 0.4827 - acc: 0.7526 - val_loss: 0.4960 - val_acc: 0.7407
Epoch 11/50

  1/254 [..............................] - ETA: 1s - loss: 0.3087 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4790 - acc: 0.7652

254/254 [==============================] - 1s 4ms/step - loss: 0.4790 - acc: 0.7652 - val_loss: 0.4949 - val_acc: 0.7371


Run completed: runs/2024-05-06T19-49-53Z

Training run 50/52 (flags = list(64, 64, 0.01, 20, 30, "sigmoid", "sigmoid", 0.5, 0.5)) 
Using run directory runs/2024-05-06T19-50-08Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/254 [..............................] - ETA: 5:07 - loss: 0.8279 - acc: 0.6500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.


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254/254 [==============================] - 2s 3ms/step - loss: 0.6686 - acc: 0.6012

254/254 [==============================] - 3s 7ms/step - loss: 0.6686 - acc: 0.6012 - val_loss: 0.5570 - val_acc: 0.7211
Epoch 2/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5538 - acc: 0.7207

254/254 [==============================] - 1s 5ms/step - loss: 0.5538 - acc: 0.7207 - val_loss: 0.5106 - val_acc: 0.7496
Epoch 3/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5245 - acc: 0.7345

254/254 [==============================] - 1s 4ms/step - loss: 0.5245 - acc: 0.7345 - val_loss: 0.4914 - val_acc: 0.7673
Epoch 4/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5147 - acc: 0.7392

254/254 [==============================] - 1s 5ms/step - loss: 0.5147 - acc: 0.7392 - val_loss: 0.4791 - val_acc: 0.7638
Epoch 5/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5153 - acc: 0.7353

254/254 [==============================] - 1s 4ms/step - loss: 0.5153 - acc: 0.7353 - val_loss: 0.4763 - val_acc: 0.7691
Epoch 6/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5115 - acc: 0.7386

254/254 [==============================] - 1s 4ms/step - loss: 0.5115 - acc: 0.7386 - val_loss: 0.4952 - val_acc: 0.7513
Epoch 7/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5110 - acc: 0.7382

254/254 [==============================] - 1s 4ms/step - loss: 0.5110 - acc: 0.7382 - val_loss: 0.4799 - val_acc: 0.7620
Epoch 8/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5048 - acc: 0.7426

254/254 [==============================] - 1s 4ms/step - loss: 0.5048 - acc: 0.7426 - val_loss: 0.4828 - val_acc: 0.7638
Epoch 9/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.5047 - acc: 0.7386

254/254 [==============================] - 1s 5ms/step - loss: 0.5047 - acc: 0.7386 - val_loss: 0.4865 - val_acc: 0.7602
Epoch 10/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5011 - acc: 0.7404

254/254 [==============================] - 1s 4ms/step - loss: 0.5011 - acc: 0.7404 - val_loss: 0.4776 - val_acc: 0.7655
Epoch 11/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4999 - acc: 0.7408

254/254 [==============================] - 1s 5ms/step - loss: 0.4999 - acc: 0.7408 - val_loss: 0.4853 - val_acc: 0.7567
Epoch 12/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.5000 - acc: 0.7489

254/254 [==============================] - 1s 4ms/step - loss: 0.5000 - acc: 0.7489 - val_loss: 0.4900 - val_acc: 0.7549
Epoch 13/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4930 - acc: 0.7465

254/254 [==============================] - 1s 4ms/step - loss: 0.4930 - acc: 0.7465 - val_loss: 0.4671 - val_acc: 0.7655
Epoch 14/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4975 - acc: 0.7500

254/254 [==============================] - 1s 5ms/step - loss: 0.4975 - acc: 0.7500 - val_loss: 0.4841 - val_acc: 0.7602
Epoch 15/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4953 - acc: 0.7447

254/254 [==============================] - 1s 4ms/step - loss: 0.4953 - acc: 0.7447 - val_loss: 0.4638 - val_acc: 0.7691
Epoch 16/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4913 - acc: 0.7489

254/254 [==============================] - 1s 5ms/step - loss: 0.4913 - acc: 0.7489 - val_loss: 0.4676 - val_acc: 0.7691
Epoch 17/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4919 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.4919 - acc: 0.7493 - val_loss: 0.4775 - val_acc: 0.7602
Epoch 18/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4960 - acc: 0.7493

254/254 [==============================] - 1s 4ms/step - loss: 0.4960 - acc: 0.7493 - val_loss: 0.4844 - val_acc: 0.7620
Epoch 19/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4938 - acc: 0.7459

254/254 [==============================] - 1s 5ms/step - loss: 0.4938 - acc: 0.7459 - val_loss: 0.4980 - val_acc: 0.7549
Epoch 20/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4925 - acc: 0.7473

254/254 [==============================] - 1s 4ms/step - loss: 0.4925 - acc: 0.7473 - val_loss: 0.4958 - val_acc: 0.7584
Epoch 21/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4885 - acc: 0.7457

254/254 [==============================] - 1s 5ms/step - loss: 0.4885 - acc: 0.7457 - val_loss: 0.4635 - val_acc: 0.7780
Epoch 22/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4920 - acc: 0.7597

254/254 [==============================] - 1s 4ms/step - loss: 0.4920 - acc: 0.7597 - val_loss: 0.5011 - val_acc: 0.7513
Epoch 23/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4910 - acc: 0.7585

254/254 [==============================] - 1s 5ms/step - loss: 0.4910 - acc: 0.7585 - val_loss: 0.4902 - val_acc: 0.7620
Epoch 24/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4880 - acc: 0.7548

254/254 [==============================] - 1s 4ms/step - loss: 0.4880 - acc: 0.7548 - val_loss: 0.4853 - val_acc: 0.7602
Epoch 25/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4875 - acc: 0.7512

254/254 [==============================] - 1s 4ms/step - loss: 0.4875 - acc: 0.7512 - val_loss: 0.4651 - val_acc: 0.7744
Epoch 26/30

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254/254 [==============================] - 1s 4ms/step - loss: 0.4898 - acc: 0.7558

254/254 [==============================] - 1s 5ms/step - loss: 0.4898 - acc: 0.7558 - val_loss: 0.4735 - val_acc: 0.7638
Epoch 27/30

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254/254 [==============================] - 1s 3ms/step - loss: 0.4858 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4858 - acc: 0.7542 - val_loss: 0.4934 - val_acc: 0.7549
Epoch 28/30

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 69/254 [=======>......................] - ETA: 0s - loss: 0.4965 - acc: 0.7391
 86/254 [=========>....................] - ETA: 0s - loss: 0.4938 - acc: 0.7483
104/254 [===========>..................] - ETA: 0s - loss: 0.4943 - acc: 0.7519
123/254 [=============>................] - ETA: 0s - loss: 0.4918 - acc: 0.7500
141/254 [===============>..............] - ETA: 0s - loss: 0.4950 - acc: 0.7486
160/254 [=================>............] - ETA: 0s - loss: 0.4937 - acc: 0.7522
166/254 [==================>...........] - ETA: 0s - loss: 0.4943 - acc: 0.7536
179/254 [====================>.........] - ETA: 0s - loss: 0.4976 - acc: 0.7520
196/254 [======================>.......] - ETA: 0s - loss: 0.4946 - acc: 0.7518
210/254 [=======================>......] - ETA: 0s - loss: 0.4953 - acc: 0.7507
219/254 [========================>.....] - ETA: 0s - loss: 0.4932 - acc: 0.7509
226/254 [=========================>....] - ETA: 0s - loss: 0.4915 - acc: 0.7511
243/254 [===========================>..] - ETA: 0s - loss: 0.4915 - acc: 0.7502
254/254 [==============================] - 1s 4ms/step - loss: 0.4905 - acc: 0.7497

254/254 [==============================] - 1s 5ms/step - loss: 0.4905 - acc: 0.7497 - val_loss: 0.4800 - val_acc: 0.7620
Epoch 29/30

  1/254 [..............................] - ETA: 1s - loss: 0.3286 - acc: 0.9500
 19/254 [=>............................] - ETA: 0s - loss: 0.4276 - acc: 0.7868
 37/254 [===>..........................] - ETA: 0s - loss: 0.4401 - acc: 0.7716
 55/254 [=====>........................] - ETA: 0s - loss: 0.4738 - acc: 0.7700
 72/254 [=======>......................] - ETA: 0s - loss: 0.4931 - acc: 0.7583
 90/254 [=========>....................] - ETA: 0s - loss: 0.4876 - acc: 0.7639
107/254 [===========>..................] - ETA: 0s - loss: 0.4896 - acc: 0.7589
123/254 [=============>................] - ETA: 0s - loss: 0.4864 - acc: 0.7573
140/254 [===============>..............] - ETA: 0s - loss: 0.4869 - acc: 0.7575
157/254 [=================>............] - ETA: 0s - loss: 0.4862 - acc: 0.7602
174/254 [===================>..........] - ETA: 0s - loss: 0.4860 - acc: 0.7595
192/254 [=====================>........] - ETA: 0s - loss: 0.4858 - acc: 0.7560
200/254 [======================>.......] - ETA: 0s - loss: 0.4844 - acc: 0.7563
210/254 [=======================>......] - ETA: 0s - loss: 0.4866 - acc: 0.7538
225/254 [=========================>....] - ETA: 0s - loss: 0.4842 - acc: 0.7560
236/254 [==========================>...] - ETA: 0s - loss: 0.4851 - acc: 0.7568
252/254 [============================>.] - ETA: 0s - loss: 0.4864 - acc: 0.7556
254/254 [==============================] - 1s 3ms/step - loss: 0.4858 - acc: 0.7558

254/254 [==============================] - 1s 4ms/step - loss: 0.4858 - acc: 0.7558 - val_loss: 0.4892 - val_acc: 0.7638
Epoch 30/30

  1/254 [..............................] - ETA: 0s - loss: 0.3507 - acc: 0.7500
 18/254 [=>............................] - ETA: 0s - loss: 0.4792 - acc: 0.7583
 36/254 [===>..........................] - ETA: 0s - loss: 0.4838 - acc: 0.7486
 52/254 [=====>........................] - ETA: 0s - loss: 0.4789 - acc: 0.7577
 69/254 [=======>......................] - ETA: 0s - loss: 0.4698 - acc: 0.7609
 76/254 [=======>......................] - ETA: 0s - loss: 0.4845 - acc: 0.7559
 94/254 [==========>...................] - ETA: 0s - loss: 0.4940 - acc: 0.7574
103/254 [===========>..................] - ETA: 0s - loss: 0.4880 - acc: 0.7578
120/254 [=============>................] - ETA: 0s - loss: 0.4836 - acc: 0.7608
137/254 [===============>..............] - ETA: 0s - loss: 0.4908 - acc: 0.7562
153/254 [=================>............] - ETA: 0s - loss: 0.4872 - acc: 0.7592
169/254 [==================>...........] - ETA: 0s - loss: 0.4886 - acc: 0.7568
185/254 [====================>.........] - ETA: 0s - loss: 0.4934 - acc: 0.7543
202/254 [======================>.......] - ETA: 0s - loss: 0.4948 - acc: 0.7515
219/254 [========================>.....] - ETA: 0s - loss: 0.4928 - acc: 0.7518
236/254 [==========================>...] - ETA: 0s - loss: 0.4935 - acc: 0.7502
254/254 [==============================] - 1s 3ms/step - loss: 0.4884 - acc: 0.7522

254/254 [==============================] - 1s 4ms/step - loss: 0.4884 - acc: 0.7522 - val_loss: 0.4671 - val_acc: 0.7726


Run completed: runs/2024-05-06T19-50-08Z

Training run 51/52 (flags = list(64, 64, 0.01, 50, 30, "sigmoid", "relu", 0.2, 0.5)) 
Using run directory runs/2024-05-06T19-50-44Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/30

  1/102 [..............................] - ETA: 1:58 - loss: 0.7919 - acc: 0.4400

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.0292s). Check your callbacks.


 15/102 [===>..........................] - ETA: 0s - loss: 0.7198 - acc: 0.5027  
 31/102 [========>.....................] - ETA: 0s - loss: 0.6874 - acc: 0.5381
 46/102 [============>.................] - ETA: 0s - loss: 0.6807 - acc: 0.5691
 62/102 [=================>............] - ETA: 0s - loss: 0.6610 - acc: 0.5942
 78/102 [=====================>........] - ETA: 0s - loss: 0.6422 - acc: 0.6205
 94/102 [==========================>...] - ETA: 0s - loss: 0.6262 - acc: 0.6406
102/102 [==============================] - 2s 3ms/step - loss: 0.6259 - acc: 0.6450

102/102 [==============================] - 2s 11ms/step - loss: 0.6259 - acc: 0.6450 - val_loss: 0.5475 - val_acc: 0.7247
Epoch 2/30

  1/102 [..............................] - ETA: 0s - loss: 0.4698 - acc: 0.6400
 15/102 [===>..........................] - ETA: 0s - loss: 0.5226 - acc: 0.7267
 29/102 [=======>......................] - ETA: 0s - loss: 0.5409 - acc: 0.7186
 37/102 [=========>....................] - ETA: 0s - loss: 0.5389 - acc: 0.7238
 47/102 [============>.................] - ETA: 0s - loss: 0.5417 - acc: 0.7272
 59/102 [================>.............] - ETA: 0s - loss: 0.5390 - acc: 0.7281
 76/102 [=====================>........] - ETA: 0s - loss: 0.5294 - acc: 0.7316
 84/102 [=======================>......] - ETA: 0s - loss: 0.5263 - acc: 0.7371
100/102 [============================>.] - ETA: 0s - loss: 0.5227 - acc: 0.7378
102/102 [==============================] - 0s 4ms/step - loss: 0.5264 - acc: 0.7368

102/102 [==============================] - 1s 6ms/step - loss: 0.5264 - acc: 0.7368 - val_loss: 0.5066 - val_acc: 0.7531
Epoch 3/30

  1/102 [..............................] - ETA: 0s - loss: 0.4909 - acc: 0.8400
 14/102 [===>..........................] - ETA: 0s - loss: 0.5057 - acc: 0.7514
 29/102 [=======>......................] - ETA: 0s - loss: 0.5213 - acc: 0.7441
 45/102 [============>.................] - ETA: 0s - loss: 0.5204 - acc: 0.7396
 61/102 [================>.............] - ETA: 0s - loss: 0.5195 - acc: 0.7367
 77/102 [=====================>........] - ETA: 0s - loss: 0.5212 - acc: 0.7384
 92/102 [==========================>...] - ETA: 0s - loss: 0.5197 - acc: 0.7385
102/102 [==============================] - 0s 3ms/step - loss: 0.5177 - acc: 0.7390

102/102 [==============================] - 1s 6ms/step - loss: 0.5177 - acc: 0.7390 - val_loss: 0.5056 - val_acc: 0.7567
Epoch 4/30

  1/102 [..............................] - ETA: 0s - loss: 0.4359 - acc: 0.7400
 14/102 [===>..........................] - ETA: 0s - loss: 0.4921 - acc: 0.7371
 20/102 [====>.........................] - ETA: 0s - loss: 0.5068 - acc: 0.7320
 35/102 [=========>....................] - ETA: 0s - loss: 0.5178 - acc: 0.7411
 40/102 [==========>...................] - ETA: 0s - loss: 0.5184 - acc: 0.7440
 56/102 [===============>..............] - ETA: 0s - loss: 0.5231 - acc: 0.7371
 71/102 [===================>..........] - ETA: 0s - loss: 0.5244 - acc: 0.7423
 86/102 [========================>.....] - ETA: 0s - loss: 0.5213 - acc: 0.7384
101/102 [============================>.] - ETA: 0s - loss: 0.5191 - acc: 0.7398
102/102 [==============================] - 0s 4ms/step - loss: 0.5186 - acc: 0.7406

102/102 [==============================] - 1s 7ms/step - loss: 0.5186 - acc: 0.7406 - val_loss: 0.4591 - val_acc: 0.7815
Epoch 5/30

  1/102 [..............................] - ETA: 0s - loss: 0.7074 - acc: 0.7200
 17/102 [====>.........................] - ETA: 0s - loss: 0.5250 - acc: 0.7659
 33/102 [========>.....................] - ETA: 0s - loss: 0.5037 - acc: 0.7545
 49/102 [=============>................] - ETA: 0s - loss: 0.4942 - acc: 0.7645
 63/102 [=================>............] - ETA: 0s - loss: 0.4991 - acc: 0.7603
 78/102 [=====================>........] - ETA: 0s - loss: 0.5001 - acc: 0.7569
 84/102 [=======================>......] - ETA: 0s - loss: 0.4984 - acc: 0.7550
100/102 [============================>.] - ETA: 0s - loss: 0.5010 - acc: 0.7536
102/102 [==============================] - 0s 4ms/step - loss: 0.5024 - acc: 0.7530

102/102 [==============================] - 1s 8ms/step - loss: 0.5024 - acc: 0.7530 - val_loss: 0.5008 - val_acc: 0.7531
Epoch 6/30

  1/102 [..............................] - ETA: 0s - loss: 0.5225 - acc: 0.7200
 16/102 [===>..........................] - ETA: 0s - loss: 0.5028 - acc: 0.7325
 32/102 [========>.....................] - ETA: 0s - loss: 0.4977 - acc: 0.7519
 48/102 [=============>................] - ETA: 0s - loss: 0.5126 - acc: 0.7425
 63/102 [=================>............] - ETA: 0s - loss: 0.5117 - acc: 0.7406
 78/102 [=====================>........] - ETA: 0s - loss: 0.5192 - acc: 0.7408
 94/102 [==========================>...] - ETA: 0s - loss: 0.5102 - acc: 0.7479
102/102 [==============================] - 0s 3ms/step - loss: 0.5094 - acc: 0.7487

102/102 [==============================] - 1s 6ms/step - loss: 0.5094 - acc: 0.7487 - val_loss: 0.5041 - val_acc: 0.7496
Epoch 7/30

  1/102 [..............................] - ETA: 0s - loss: 0.5676 - acc: 0.6800
 12/102 [==>...........................] - ETA: 0s - loss: 0.4756 - acc: 0.7500
 20/102 [====>.........................] - ETA: 0s - loss: 0.4804 - acc: 0.7550
 37/102 [=========>....................] - ETA: 0s - loss: 0.4809 - acc: 0.7470
 49/102 [=============>................] - ETA: 0s - loss: 0.4875 - acc: 0.7522
 65/102 [==================>...........] - ETA: 0s - loss: 0.4965 - acc: 0.7446
 81/102 [======================>.......] - ETA: 0s - loss: 0.4974 - acc: 0.7469
 95/102 [==========================>...] - ETA: 0s - loss: 0.4999 - acc: 0.7446
102/102 [==============================] - 0s 4ms/step - loss: 0.5018 - acc: 0.7445

102/102 [==============================] - 1s 7ms/step - loss: 0.5018 - acc: 0.7445 - val_loss: 0.4972 - val_acc: 0.7549
Epoch 8/30

  1/102 [..............................] - ETA: 0s - loss: 0.5212 - acc: 0.6800
 16/102 [===>..........................] - ETA: 0s - loss: 0.5057 - acc: 0.7513
 32/102 [========>.....................] - ETA: 0s - loss: 0.4913 - acc: 0.7625
 48/102 [=============>................] - ETA: 0s - loss: 0.4964 - acc: 0.7592
 63/102 [=================>............] - ETA: 0s - loss: 0.5079 - acc: 0.7505
 79/102 [======================>.......] - ETA: 0s - loss: 0.5040 - acc: 0.7486
 94/102 [==========================>...] - ETA: 0s - loss: 0.5019 - acc: 0.7485
102/102 [==============================] - 0s 3ms/step - loss: 0.4966 - acc: 0.7504

102/102 [==============================] - 1s 6ms/step - loss: 0.4966 - acc: 0.7504 - val_loss: 0.4558 - val_acc: 0.7815
Epoch 9/30

  1/102 [..............................] - ETA: 0s - loss: 0.5580 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.4781 - acc: 0.7750
 33/102 [========>.....................] - ETA: 0s - loss: 0.5050 - acc: 0.7642
 50/102 [=============>................] - ETA: 0s - loss: 0.5069 - acc: 0.7556
 66/102 [==================>...........] - ETA: 0s - loss: 0.5145 - acc: 0.7436
 81/102 [======================>.......] - ETA: 0s - loss: 0.5037 - acc: 0.7467
 97/102 [===========================>..] - ETA: 0s - loss: 0.5006 - acc: 0.7474
102/102 [==============================] - 0s 3ms/step - loss: 0.4988 - acc: 0.7489

102/102 [==============================] - 1s 7ms/step - loss: 0.4988 - acc: 0.7489 - val_loss: 0.4758 - val_acc: 0.7655
Epoch 10/30

  1/102 [..............................] - ETA: 0s - loss: 0.3579 - acc: 0.8400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5094 - acc: 0.7588
 31/102 [========>.....................] - ETA: 0s - loss: 0.4895 - acc: 0.7652
 47/102 [============>.................] - ETA: 0s - loss: 0.4855 - acc: 0.7604
 62/102 [=================>............] - ETA: 0s - loss: 0.4857 - acc: 0.7568
 78/102 [=====================>........] - ETA: 0s - loss: 0.4896 - acc: 0.7569
 91/102 [=========================>....] - ETA: 0s - loss: 0.4914 - acc: 0.7549
 99/102 [============================>.] - ETA: 0s - loss: 0.4956 - acc: 0.7547
102/102 [==============================] - 0s 4ms/step - loss: 0.4968 - acc: 0.7532

102/102 [==============================] - 1s 7ms/step - loss: 0.4968 - acc: 0.7532 - val_loss: 0.4890 - val_acc: 0.7638
Epoch 11/30

  1/102 [..............................] - ETA: 0s - loss: 0.7354 - acc: 0.7000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5200 - acc: 0.7487
 33/102 [========>.....................] - ETA: 0s - loss: 0.5079 - acc: 0.7503
 49/102 [=============>................] - ETA: 0s - loss: 0.4985 - acc: 0.7506
 64/102 [=================>............] - ETA: 0s - loss: 0.5001 - acc: 0.7469
 79/102 [======================>.......] - ETA: 0s - loss: 0.4963 - acc: 0.7481
 93/102 [==========================>...] - ETA: 0s - loss: 0.4958 - acc: 0.7492
102/102 [==============================] - 0s 4ms/step - loss: 0.4928 - acc: 0.7508

102/102 [==============================] - 1s 6ms/step - loss: 0.4928 - acc: 0.7508 - val_loss: 0.4662 - val_acc: 0.7762
Epoch 12/30

  1/102 [..............................] - ETA: 0s - loss: 0.5342 - acc: 0.7600
 15/102 [===>..........................] - ETA: 0s - loss: 0.4986 - acc: 0.7573
 31/102 [========>.....................] - ETA: 0s - loss: 0.5052 - acc: 0.7426
 47/102 [============>.................] - ETA: 0s - loss: 0.5004 - acc: 0.7387
 64/102 [=================>............] - ETA: 0s - loss: 0.4996 - acc: 0.7409
 80/102 [======================>.......] - ETA: 0s - loss: 0.4900 - acc: 0.7437
 96/102 [===========================>..] - ETA: 0s - loss: 0.4903 - acc: 0.7504
102/102 [==============================] - 0s 3ms/step - loss: 0.4902 - acc: 0.7514

102/102 [==============================] - 1s 6ms/step - loss: 0.4902 - acc: 0.7514 - val_loss: 0.5036 - val_acc: 0.7513
Epoch 13/30

  1/102 [..............................] - ETA: 0s - loss: 0.3579 - acc: 0.8000
 16/102 [===>..........................] - ETA: 0s - loss: 0.5056 - acc: 0.7275
 27/102 [======>.......................] - ETA: 0s - loss: 0.4983 - acc: 0.7430
 39/102 [==========>...................] - ETA: 0s - loss: 0.4950 - acc: 0.7503
 54/102 [==============>...............] - ETA: 0s - loss: 0.5001 - acc: 0.7489
 69/102 [===================>..........] - ETA: 0s - loss: 0.5004 - acc: 0.7493
 85/102 [========================>.....] - ETA: 0s - loss: 0.5014 - acc: 0.7508
101/102 [============================>.] - ETA: 0s - loss: 0.4965 - acc: 0.7539
102/102 [==============================] - 0s 4ms/step - loss: 0.4962 - acc: 0.7542

102/102 [==============================] - 1s 6ms/step - loss: 0.4962 - acc: 0.7542 - val_loss: 0.4784 - val_acc: 0.7691
Epoch 14/30

  1/102 [..............................] - ETA: 0s - loss: 0.5356 - acc: 0.8000
 17/102 [====>.........................] - ETA: 0s - loss: 0.5017 - acc: 0.7600
 33/102 [========>.....................] - ETA: 0s - loss: 0.4889 - acc: 0.7612
 49/102 [=============>................] - ETA: 0s - loss: 0.4834 - acc: 0.7543
 65/102 [==================>...........] - ETA: 0s - loss: 0.4807 - acc: 0.7615
 81/102 [======================>.......] - ETA: 0s - loss: 0.4826 - acc: 0.7590
 96/102 [===========================>..] - ETA: 0s - loss: 0.4902 - acc: 0.7571
102/102 [==============================] - 0s 3ms/step - loss: 0.4919 - acc: 0.7562

102/102 [==============================] - 1s 7ms/step - loss: 0.4919 - acc: 0.7562 - val_loss: 0.4993 - val_acc: 0.7567
Epoch 15/30

  1/102 [..............................] - ETA: 0s - loss: 0.7890 - acc: 0.6400
 16/102 [===>..........................] - ETA: 0s - loss: 0.5066 - acc: 0.7437
 33/102 [========>.....................] - ETA: 0s - loss: 0.4998 - acc: 0.7406
 49/102 [=============>................] - ETA: 0s - loss: 0.4935 - acc: 0.7424
 65/102 [==================>...........] - ETA: 0s - loss: 0.4845 - acc: 0.7557
 81/102 [======================>.......] - ETA: 0s - loss: 0.4896 - acc: 0.7533
 97/102 [===========================>..] - ETA: 0s - loss: 0.4934 - acc: 0.7491
102/102 [==============================] - 0s 3ms/step - loss: 0.4911 - acc: 0.7497

102/102 [==============================] - 1s 5ms/step - loss: 0.4911 - acc: 0.7497 - val_loss: 0.4957 - val_acc: 0.7567
Epoch 16/30

  1/102 [..............................] - ETA: 0s - loss: 0.5457 - acc: 0.7600
 17/102 [====>.........................] - ETA: 0s - loss: 0.5019 - acc: 0.7412
 34/102 [=========>....................] - ETA: 0s - loss: 0.5123 - acc: 0.7494
 43/102 [===========>..................] - ETA: 0s - loss: 0.4992 - acc: 0.7521
 58/102 [================>.............] - ETA: 0s - loss: 0.4923 - acc: 0.7531
 67/102 [==================>...........] - ETA: 0s - loss: 0.4894 - acc: 0.7549
 81/102 [======================>.......] - ETA: 0s - loss: 0.4867 - acc: 0.7578
 97/102 [===========================>..] - ETA: 0s - loss: 0.4935 - acc: 0.7563
102/102 [==============================] - 0s 4ms/step - loss: 0.4960 - acc: 0.7538

102/102 [==============================] - 1s 7ms/step - loss: 0.4960 - acc: 0.7538 - val_loss: 0.5081 - val_acc: 0.7549
Epoch 17/30

  1/102 [..............................] - ETA: 0s - loss: 0.5604 - acc: 0.7800
 16/102 [===>..........................] - ETA: 0s - loss: 0.4749 - acc: 0.7650
 32/102 [========>.....................] - ETA: 0s - loss: 0.4751 - acc: 0.7569
 48/102 [=============>................] - ETA: 0s - loss: 0.4855 - acc: 0.7658
 64/102 [=================>............] - ETA: 0s - loss: 0.4859 - acc: 0.7600
 79/102 [======================>.......] - ETA: 0s - loss: 0.4857 - acc: 0.7592
 95/102 [==========================>...] - ETA: 0s - loss: 0.4892 - acc: 0.7573
102/102 [==============================] - 0s 3ms/step - loss: 0.4884 - acc: 0.7566

102/102 [==============================] - 1s 6ms/step - loss: 0.4884 - acc: 0.7566 - val_loss: 0.4894 - val_acc: 0.7655
Epoch 18/30

  1/102 [..............................] - ETA: 0s - loss: 0.4830 - acc: 0.7400
 16/102 [===>..........................] - ETA: 0s - loss: 0.4817 - acc: 0.7387
 32/102 [========>.....................] - ETA: 0s - loss: 0.4944 - acc: 0.7431
 49/102 [=============>................] - ETA: 0s - loss: 0.4981 - acc: 0.7486
 54/102 [==============>...............] - ETA: 0s - loss: 0.4926 - acc: 0.7481
 70/102 [===================>..........] - ETA: 0s - loss: 0.4948 - acc: 0.7486
 84/102 [=======================>......] - ETA: 0s - loss: 0.4915 - acc: 0.7512
 95/102 [==========================>...] - ETA: 0s - loss: 0.4922 - acc: 0.7497
102/102 [==============================] - 0s 4ms/step - loss: 0.4888 - acc: 0.7520

102/102 [==============================] - 1s 6ms/step - loss: 0.4888 - acc: 0.7520 - val_loss: 0.4965 - val_acc: 0.7496


Run completed: runs/2024-05-06T19-50-44Z

Training run 52/52 (flags = list(64, 16, 0.001, 20, 50, "relu", "tanh", 0.5, 0.2)) 
Using run directory runs/2024-05-06T19-50-58Z


> Flags <- flags(
+   flag_numeric("nodes", 128),
+   flag_numeric("nodes2", 32),
+   flag_numeric("batch_size", 50),
+   flag_string("activation", "r ..." ... [TRUNCATED] 

> callbacks = list(callback_early_stopping(monitor = "val_loss",patience = 10, restore_best_weights = TRUE))

> model = keras_model_sequential()

> model %>%
+   layer_dense(units = Flags$nodes, activation = Flags$activation,input_shape = dim(x_train)[2]) %>%
+   layer_dropout(Flags$dropout) %>% .... [TRUNCATED] 

> model %>% compile(optimizer = optimizer_adam(lr = Flags$learning_rate), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.


> model %>% fit(
+   as.matrix(x_train), y_train, epochs = Flags$epochs, batch_size = Flags$batch_size, validation_data=list(as.matrix(x_test), y_test .... [TRUNCATED] 
Epoch 1/50

  1/254 [..............................] - ETA: 5:05 - loss: 0.8740 - acc: 0.4500

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0074s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.
WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0074s vs `on_train_batch_end` time: 0.0293s). Check your callbacks.


  9/254 [>.............................] - ETA: 1s - loss: 0.7049 - acc: 0.5222  
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254/254 [==============================] - 2s 3ms/step - loss: 0.5627 - acc: 0.7094

254/254 [==============================] - 3s 7ms/step - loss: 0.5627 - acc: 0.7094 - val_loss: 0.4867 - val_acc: 0.7567
Epoch 2/50

  1/254 [..............................] - ETA: 1s - loss: 0.4816 - acc: 0.7500
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241/254 [===========================>..] - ETA: 0s - loss: 0.5161 - acc: 0.7390
254/254 [==============================] - 1s 3ms/step - loss: 0.5171 - acc: 0.7400

254/254 [==============================] - 1s 4ms/step - loss: 0.5171 - acc: 0.7400 - val_loss: 0.4692 - val_acc: 0.7691
Epoch 3/50

  1/254 [..............................] - ETA: 1s - loss: 0.5113 - acc: 0.8000
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137/254 [===============>..............] - ETA: 0s - loss: 0.5101 - acc: 0.7394
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252/254 [============================>.] - ETA: 0s - loss: 0.5064 - acc: 0.7466
254/254 [==============================] - 1s 4ms/step - loss: 0.5063 - acc: 0.7461

254/254 [==============================] - 1s 5ms/step - loss: 0.5063 - acc: 0.7461 - val_loss: 0.4858 - val_acc: 0.7549
Epoch 4/50

  1/254 [..............................] - ETA: 0s - loss: 1.1092 - acc: 0.5000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4999 - acc: 0.7414

254/254 [==============================] - 1s 4ms/step - loss: 0.4999 - acc: 0.7414 - val_loss: 0.4735 - val_acc: 0.7726
Epoch 5/50

  1/254 [..............................] - ETA: 1s - loss: 0.4486 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4956 - acc: 0.7469

254/254 [==============================] - 1s 5ms/step - loss: 0.4956 - acc: 0.7469 - val_loss: 0.4936 - val_acc: 0.7620
Epoch 6/50

  1/254 [..............................] - ETA: 1s - loss: 0.6309 - acc: 0.5000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4914 - acc: 0.7508

254/254 [==============================] - 1s 4ms/step - loss: 0.4914 - acc: 0.7508 - val_loss: 0.4930 - val_acc: 0.7407
Epoch 7/50

  1/254 [..............................] - ETA: 1s - loss: 0.4336 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4887 - acc: 0.7465

254/254 [==============================] - 1s 5ms/step - loss: 0.4887 - acc: 0.7465 - val_loss: 0.4868 - val_acc: 0.7602
Epoch 8/50

  1/254 [..............................] - ETA: 1s - loss: 0.3616 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4840 - acc: 0.7471

254/254 [==============================] - 1s 5ms/step - loss: 0.4840 - acc: 0.7471 - val_loss: 0.4919 - val_acc: 0.7549
Epoch 9/50

  1/254 [..............................] - ETA: 0s - loss: 0.3933 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4925 - acc: 0.7504

254/254 [==============================] - 1s 4ms/step - loss: 0.4925 - acc: 0.7504 - val_loss: 0.4929 - val_acc: 0.7549
Epoch 10/50

  1/254 [..............................] - ETA: 0s - loss: 0.5608 - acc: 0.7000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4866 - acc: 0.7500

254/254 [==============================] - 1s 4ms/step - loss: 0.4866 - acc: 0.7500 - val_loss: 0.4903 - val_acc: 0.7531
Epoch 11/50

  1/254 [..............................] - ETA: 0s - loss: 0.4873 - acc: 0.7500
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254/254 [==============================] - 1s 3ms/step - loss: 0.4830 - acc: 0.7475

254/254 [==============================] - 1s 4ms/step - loss: 0.4830 - acc: 0.7475 - val_loss: 0.4936 - val_acc: 0.7478
Epoch 12/50

  1/254 [..............................] - ETA: 0s - loss: 0.6351 - acc: 0.8000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4808 - acc: 0.7552

254/254 [==============================] - 1s 4ms/step - loss: 0.4808 - acc: 0.7552 - val_loss: 0.4665 - val_acc: 0.7655
Epoch 13/50

  1/254 [..............................] - ETA: 0s - loss: 0.4461 - acc: 0.7000
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254/254 [==============================] - 1s 4ms/step - loss: 0.4797 - acc: 0.7558

254/254 [==============================] - 1s 4ms/step - loss: 0.4797 - acc: 0.7558 - val_loss: 0.4936 - val_acc: 0.7531
Epoch 14/50

  1/254 [..............................] - ETA: 0s - loss: 0.4888 - acc: 0.9000
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254/254 [==============================] - 1s 3ms/step - loss: 0.4721 - acc: 0.7633

254/254 [==============================] - 1s 4ms/step - loss: 0.4721 - acc: 0.7633 - val_loss: 0.4824 - val_acc: 0.7602
Epoch 15/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4769 - acc: 0.7569

254/254 [==============================] - 1s 5ms/step - loss: 0.4769 - acc: 0.7569 - val_loss: 0.4803 - val_acc: 0.7478
Epoch 16/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4763 - acc: 0.7658

254/254 [==============================] - 1s 4ms/step - loss: 0.4763 - acc: 0.7658 - val_loss: 0.4845 - val_acc: 0.7496
Epoch 17/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4749 - acc: 0.7534

254/254 [==============================] - 1s 4ms/step - loss: 0.4749 - acc: 0.7534 - val_loss: 0.4748 - val_acc: 0.7638
Epoch 18/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4723 - acc: 0.7589

254/254 [==============================] - 1s 5ms/step - loss: 0.4723 - acc: 0.7589 - val_loss: 0.4805 - val_acc: 0.7567
Epoch 19/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4735 - acc: 0.7542

254/254 [==============================] - 1s 4ms/step - loss: 0.4735 - acc: 0.7542 - val_loss: 0.4800 - val_acc: 0.7655
Epoch 20/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4694 - acc: 0.7587

254/254 [==============================] - 1s 5ms/step - loss: 0.4694 - acc: 0.7587 - val_loss: 0.4955 - val_acc: 0.7460
Epoch 21/50

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254/254 [==============================] - 1s 3ms/step - loss: 0.4728 - acc: 0.7540

254/254 [==============================] - 1s 4ms/step - loss: 0.4728 - acc: 0.7540 - val_loss: 0.4918 - val_acc: 0.7442
Epoch 22/50

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254/254 [==============================] - 1s 4ms/step - loss: 0.4694 - acc: 0.7603 - val_loss: 0.4717 - val_acc: 0.7709


Run completed: runs/2024-05-06T19-50-58Z

set.seed(1)
runs= runs[order(runs$metric_val_loss),]
runs

Data frame: 52 x 29

# ... with 42 more rows
# ... with 24 more columns:
#   flag_nodes, flag_nodes2, flag_batch_size, flag_activation, flag_activation2,
#   flag_learning_rate, flag_epochs, flag_dropout, flag_dropout2, epochs, epochs_completed,
#   metrics, model, loss_function, optimizer, learning_rate, script, start, end, completed,
#   output, source_code, context, type

set.seed(1)
runs[which.min(runs$metric_val_loss),]

 $ run_dir           : chr "runs/2024-05-06T19-50-08Z"
 $ metric_loss       : num 0.488
 $ metric_acc        : num 0.752
 $ metric_val_loss   : num 0.467
 $ metric_val_acc    : num 0.773
 $ flag_nodes        : int 64
 $ flag_nodes2       : int 64
 $ flag_batch_size   : int 20
 $ flag_activation   : chr "sigmoid"
 $ flag_activation2  : chr "sigmoid"
 $ flag_learning_rate: num 0.01
 $ flag_epochs       : int 30
 $ flag_dropout      : num 0.5
 $ flag_dropout2     : num 0.5
 $ epochs            : int 30
 $ epochs_completed  : int 30
 $ metrics           : chr "(metrics data frame)"
 $ model             : chr "(model summary)"
 $ loss_function     : chr "binary_crossentropy"
 $ optimizer         : chr "<keras.src.optimizers.adam.Adam object at 0x0000019AFA3CD910>"
 $ learning_rate     : num 0.001
 $ script            : chr "Projectscript.R"
 $ start             : POSIXct[1:1], format: "2024-05-06 19:50:08"
 $ end               : POSIXct[1:1], format: "2024-05-06 19:50:44"
 $ completed         : logi TRUE
 $ output            : chr "(script ouptut)"
 $ source_code       : chr "(source archive)"
 $ context           : chr "local"
 $ type              : chr "training"

set.seed(1)
view_run(runs[which.min(runs$metric_val_loss),])

Warning: argument 'compressed' is ignored for the internal method

Lets train our best model nodes1 - 64 nodes2-64 epochs-30 batch_size=20 activation function1 = “sigmoid” activation function2 = “sigmoid” dropout rate1 = 0.5 dropout rate2 = 0.5 learning rate = 0.001

ts$Churn=as.numeric(ts$Churn)-1

Converting Ordered columns to numeric


ts$tenure <- as.numeric(factor(ts$tenure, levels =
c("0-1 years", "1-2 years", "2-3years",  "3-4 years", "4-5 years", "5-6years" ), ordered = TRUE))-1

ts_labels<-ts$Churn


ts= data.frame(one_hot(data.table(ts)))

ts$Churn<-NULL

numeric_cols<-names(td[sapply(td, is.numeric) & !sapply(td, is.integer)])

col_means_train <- attr(scale(td[,numeric_cols]), "scaled:center")
col_stddevs_train <- attr( scale(td[,numeric_cols]), "scaled:scale")
td[numeric_cols]= scale(td[numeric_cols])
ts[numeric_cols]<- scale(ts[numeric_cols], center = col_means_train,
scale = col_stddevs_train)

set.seed(1)
model_nn <- keras_model_sequential() %>%
layer_dense(units = 64, activation = "sigmoid",
input_shape = dim(td)[2]) %>%
layer_dropout(0.5)%>%
layer_dense(units = 64,activation="sigmoid" ) %>%
layer_dropout(0.5)%>%
layer_dense(units = 1, activation="sigmoid")
model_nn

Model: "sequential"
_________________________________________________________________________________________________________
 Layer (type)                                  Output Shape                              Param #         
=========================================================================================================
 dense_2 (Dense)                               (None, 64)                                3008            
 dropout_1 (Dropout)                           (None, 64)                                0               
 dense_1 (Dense)                               (None, 64)                                4160            
 dropout (Dropout)                             (None, 64)                                0               
 dense (Dense)                                 (None, 1)                                 65              
=========================================================================================================
Total params: 7233 (28.25 KB)
Trainable params: 7233 (28.25 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________________________________________________

set.seed(1)

model_nn %>% compile(optimizer = optimizer_adam(lr = 0.001), loss = "binary_crossentropy", metrics = "acc")

WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.

model_nn %>% fit(
  as.matrix(td), labels, epochs = 30, batch_size = 20, validation_data=list(as.matrix(ts), ts_labels))

Epoch 1/30

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282/282 [==============================] - 2s 3ms/step - loss: 0.5750 - acc: 0.7127

282/282 [==============================] - 3s 6ms/step - loss: 0.5750 - acc: 0.7127 - val_loss: 0.4700 - val_acc: 0.7690
Epoch 2/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4714 - acc: 0.7679

282/282 [==============================] - 1s 4ms/step - loss: 0.4714 - acc: 0.7679 - val_loss: 0.4553 - val_acc: 0.7711
Epoch 3/30

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270/282 [===========================>..] - ETA: 0s - loss: 0.4558 - acc: 0.7746
282/282 [==============================] - 1s 3ms/step - loss: 0.4558 - acc: 0.7757

282/282 [==============================] - 1s 4ms/step - loss: 0.4558 - acc: 0.7757 - val_loss: 0.4516 - val_acc: 0.7768
Epoch 4/30

  1/282 [..............................] - ETA: 1s - loss: 0.3434 - acc: 0.8000
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282/282 [==============================] - 1s 3ms/step - loss: 0.4410 - acc: 0.7850

282/282 [==============================] - 1s 4ms/step - loss: 0.4410 - acc: 0.7850 - val_loss: 0.4518 - val_acc: 0.7733
Epoch 5/30

  1/282 [..............................] - ETA: 1s - loss: 0.6238 - acc: 0.6500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4403 - acc: 0.7876

282/282 [==============================] - 1s 4ms/step - loss: 0.4403 - acc: 0.7876 - val_loss: 0.4500 - val_acc: 0.7797
Epoch 6/30

  1/282 [..............................] - ETA: 11s - loss: 0.5105 - acc: 0.7500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4321 - acc: 0.7965

282/282 [==============================] - 1s 4ms/step - loss: 0.4321 - acc: 0.7965 - val_loss: 0.4507 - val_acc: 0.7726
Epoch 7/30

  1/282 [..............................] - ETA: 9s - loss: 0.4941 - acc: 0.7500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4357 - acc: 0.7963

282/282 [==============================] - 1s 4ms/step - loss: 0.4357 - acc: 0.7963 - val_loss: 0.4477 - val_acc: 0.7783
Epoch 8/30

  1/282 [..............................] - ETA: 0s - loss: 0.7099 - acc: 0.5500
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265/282 [===========================>..] - ETA: 0s - loss: 0.4323 - acc: 0.8000
282/282 [==============================] - 1s 3ms/step - loss: 0.4321 - acc: 0.7993

282/282 [==============================] - 1s 4ms/step - loss: 0.4321 - acc: 0.7993 - val_loss: 0.4475 - val_acc: 0.7783
Epoch 9/30

  1/282 [..............................] - ETA: 10s - loss: 0.4215 - acc: 0.6500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4296 - acc: 0.7972

282/282 [==============================] - 1s 4ms/step - loss: 0.4296 - acc: 0.7972 - val_loss: 0.4477 - val_acc: 0.7818
Epoch 10/30

  1/282 [..............................] - ETA: 0s - loss: 0.3011 - acc: 0.8500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4332 - acc: 0.7960

282/282 [==============================] - 1s 4ms/step - loss: 0.4332 - acc: 0.7960 - val_loss: 0.4487 - val_acc: 0.7747
Epoch 11/30

  1/282 [..............................] - ETA: 1s - loss: 0.4957 - acc: 0.8000
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282/282 [==============================] - 1s 3ms/step - loss: 0.4302 - acc: 0.7949

282/282 [==============================] - 1s 4ms/step - loss: 0.4302 - acc: 0.7949 - val_loss: 0.4462 - val_acc: 0.7790
Epoch 12/30

  1/282 [..............................] - ETA: 0s - loss: 0.4419 - acc: 0.7500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4279 - acc: 0.8000

282/282 [==============================] - 1s 4ms/step - loss: 0.4279 - acc: 0.8000 - val_loss: 0.4477 - val_acc: 0.7818
Epoch 13/30

  1/282 [..............................] - ETA: 1s - loss: 0.3450 - acc: 0.7500
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282/282 [==============================] - 1s 3ms/step - loss: 0.4279 - acc: 0.8013

282/282 [==============================] - 1s 4ms/step - loss: 0.4279 - acc: 0.8013 - val_loss: 0.4480 - val_acc: 0.7761
Epoch 14/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4295 - acc: 0.8002

282/282 [==============================] - 1s 4ms/step - loss: 0.4295 - acc: 0.8002 - val_loss: 0.4469 - val_acc: 0.7839
Epoch 15/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4242 - acc: 0.8023

282/282 [==============================] - 1s 4ms/step - loss: 0.4242 - acc: 0.8023 - val_loss: 0.4461 - val_acc: 0.7747
Epoch 16/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4280 - acc: 0.7965

282/282 [==============================] - 1s 4ms/step - loss: 0.4280 - acc: 0.7965 - val_loss: 0.4450 - val_acc: 0.7783
Epoch 17/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4233 - acc: 0.8018

282/282 [==============================] - 1s 4ms/step - loss: 0.4233 - acc: 0.8018 - val_loss: 0.4452 - val_acc: 0.7783
Epoch 18/30

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282/282 [==============================] - 1s 3ms/step - loss: 0.4268 - acc: 0.7977

282/282 [==============================] - 1s 4ms/step - loss: 0.4268 - acc: 0.7977 - val_loss: 0.4458 - val_acc: 0.7775
Epoch 19/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4220 - acc: 0.8041 - val_loss: 0.4458 - val_acc: 0.7754
Epoch 20/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4234 - acc: 0.8038 - val_loss: 0.4448 - val_acc: 0.7804
Epoch 21/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4204 - acc: 0.8011 - val_loss: 0.4455 - val_acc: 0.7733
Epoch 22/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4247 - acc: 0.8046 - val_loss: 0.4424 - val_acc: 0.7775
Epoch 23/30

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282/282 [==============================] - 1s 5ms/step - loss: 0.4198 - acc: 0.8066 - val_loss: 0.4443 - val_acc: 0.7811
Epoch 24/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4255 - acc: 0.7990 - val_loss: 0.4428 - val_acc: 0.7818
Epoch 25/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4203 - acc: 0.8059 - val_loss: 0.4421 - val_acc: 0.7790
Epoch 26/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4190 - acc: 0.8038 - val_loss: 0.4434 - val_acc: 0.7790
Epoch 27/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4220 - acc: 0.8031 - val_loss: 0.4438 - val_acc: 0.7804
Epoch 28/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4228 - acc: 0.8027 - val_loss: 0.4432 - val_acc: 0.7783
Epoch 29/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4232 - acc: 0.8048 - val_loss: 0.4418 - val_acc: 0.7797
Epoch 30/30

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282/282 [==============================] - 1s 4ms/step - loss: 0.4239 - acc: 0.8077 - val_loss: 0.4455 - val_acc: 0.7790

set.seed(1)
predicted_probs = model_nn%>% predict(as.matrix(ts))


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44/44 [==============================] - 0s 2ms/step

set.seed(1)
predictions_nn = prediction(predicted_probs, ts_labels)
performance(predictions_nn, measure = "auc")@y.values

[[1]]
[1] 0.8207396

set.seed(1)
predicted_labels = factor(ifelse(predicted_probs>0.5, "1", "0"))
confusionMatrix(predicted_labels, as.factor(ts_labels), positive="1", mode="everything")

Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0 923 200
         1 111 173
                                          
               Accuracy : 0.779           
                 95% CI : (0.7564, 0.8004)
    No Information Rate : 0.7349          
    P-Value [Acc > NIR] : 7.803e-05       
                                          
                  Kappa : 0.3859          
                                          
 Mcnemar's Test P-Value : 6.037e-07       
                                          
            Sensitivity : 0.4638          
            Specificity : 0.8926          
         Pos Pred Value : 0.6092          
         Neg Pred Value : 0.8219          
              Precision : 0.6092          
                 Recall : 0.4638          
                     F1 : 0.5266          
             Prevalence : 0.2651          
         Detection Rate : 0.1230          
   Detection Prevalence : 0.2018          
      Balanced Accuracy : 0.6782          
                                          
       'Positive' Class : 1

The AUC of the model is 82.7 The mectrics of the model are precision - 0.60 recall- 0.46 f1 score- 0.52

KNN (base): Achieved an AUC of 77 with moderate precision, recall, and F1-score. Lasso, Ridge, Enet: All three achieved similar performance with slightly higher AUC compared to KNN. Random Forest (RF): Achieved better AUC with class weighting (CW) and SMOTE techniques. Gradient Boosting Machine (GBM): GBM showed the highest AUC among all models, especially when combined with SMOTE, with good precision, recall, and F1-score. Support Vector Machine (SVM): SVM with linear and radial kernels performed moderately well but didn’t outperform GBM. Neural Network: The initial neural network model showed decent performance, which was further improved with class weighting (CW).

In conclusion, the Gradient Boosting Machine with SMOTE stands out as the best-performing model for churn prediction, with a high AUC and a good balance between precision and recall. This model could be deployed for churn prediction tasks to effectively identify potential churners.

###shapely Values

mod <- Predictor$new(smote_gbm, data = test_data, type = "prob")


shapley <- Shapley$new(mod, x.interest = test_data[1:100, ])


print(shapley)

Interpretation method:  Shapley 
Predicted value: 0.963790, Average prediction: 0.704131 (diff = 0.259660) Predicted value: 0.036210, Average prediction: 0.295869 (diff = -0.259660)

Analysed predictor: 
Prediction task: classification 
Classes:  

Analysed data:
Sampling from data.frame with 1407 rows and 19 columns.


Head of results:

shapley$results

plot(shapley)

test_data$predicted_probabilities = predict(smote_gbm, test_data, type="prob")$Yes
dem_parity(data= test_data,outcome='Churn',group='SeniorCitizen',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                        No         Yes
Positively classified  257 109.0000000
Demographic Parity       1   0.4241245
Group size            1176 231.0000000

$Metric_plot

$Probability_plot

equal_odds(data= test_data,outcome='Churn',group='SeniorCitizen',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                         No        Yes
Sensitivity       0.5438596   0.750000
Equalized odds    1.0000000   1.379032
Group size     1176.0000000 231.000000

$Metric_plot

$Probability_plot

pred_rate_parity(data= test_data,outcome='Churn',group='SeniorCitizen',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                                 No         Yes
Precision                 0.6031128   0.6055046
Predictive Rate Parity    1.0000000   1.0039657
Group size             1176.0000000 231.0000000

$Metric_plot

$Probability_plot

test_data$predicted_probabilities = predict(smote_gbm, test_data, type="prob")$Yes
dem_parity(data= test_data,outcome='Churn',group='Partner',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                       No         Yes
Positively classified 267  99.0000000
Demographic Parity      1   0.3707865
Group size            714 693.0000000

$Metric_plot

$Probability_plot

equal_odds(data= test_data,outcome='Churn',group='Partner',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                  No         Yes
Sensitivity      0.7   0.4195804
Equalized odds   1.0   0.5994006
Group size     714.0 693.0000000

$Metric_plot

$Probability_plot

pred_rate_parity(data= test_data,outcome='Churn',group='Partner',probs='predicted_probabilities',cutoff=0.5,base='No')

$Metric
                                No         Yes
Precision                0.6029963   0.6060606
Predictive Rate Parity   1.0000000   1.0050819
Group size             714.0000000 693.0000000

$Metric_plot

$Probability_plot

Project

Vamshi

2024-05-03

Telecom Customer Churn Prediction.

Section1: Data Cleaning & Exploration

Loading dataset and Libraries

Exploratory Data Analysis

Splitting Dataset to Train and Test sets

Section2: Training ML models